Class swarmauri_standard.metrics.FrobeniusMetric.FrobeniusMetric

swarmauri_standard.metrics.FrobeniusMetric.FrobeniusMetric

Bases: MetricBase

Implementation of the Frobenius metric for matrices.

The Frobenius metric calculates the distance between two matrices as the square root of the sum of squared differences of their entries.

Attributes

type : Literal["FrobeniusMetric"] The specific type of metric. resource : str, optional The resource type, defaults to METRIC.

type class-attribute instance-attribute

type = 'FrobeniusMetric'

resource class-attribute instance-attribute

resource = Field(default=METRIC.value)

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 Frobenius distance between two matrices.

Parameters

x : MetricInput First matrix y : MetricInput Second matrix

Returns

float The Frobenius distance between x and y

Raises

ValueError If matrices have different shapes TypeError If inputs are not matrices

Source code in swarmauri_standard/metrics/FrobeniusMetric.py

def distance(self, x: MetricInput, y: MetricInput) -> float:
    """
    Calculate the Frobenius distance between two matrices.

    Parameters
    ----------
    x : MetricInput
        First matrix
    y : MetricInput
        Second matrix

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

    Raises
    ------
    ValueError
        If matrices have different shapes
    TypeError
        If inputs are not matrices
    """
    logger.debug(
        f"Calculating Frobenius distance between matrices of types {type(x)} and {type(y)}"
    )

    # Convert inputs to numpy arrays for easier processing
    if isinstance(x, IMatrix):
        x_array = np.array(x)
    elif isinstance(x, (np.ndarray, list)):
        x_array = np.array(x)
    else:
        raise TypeError(f"Unsupported type for Frobenius metric: {type(x)}")

    if isinstance(y, IMatrix):
        y_array = np.array(y)
    elif isinstance(y, (np.ndarray, list)):
        y_array = np.array(y)
    else:
        raise TypeError(f"Unsupported type for Frobenius metric: {type(y)}")

    # Check if matrices have the same shape
    if x_array.shape != y_array.shape:
        error_msg = f"Matrices must have the same shape. Got {x_array.shape} and {y_array.shape}"
        logger.error(error_msg)
        raise ValueError(error_msg)

    # Calculate the Frobenius distance
    # sqrt(sum((x_ij - y_ij)^2))
    diff = x_array - y_array
    frobenius_distance = np.sqrt(np.sum(diff * diff))

    logger.debug(f"Frobenius distance calculated: {frobenius_distance}")
    return float(frobenius_distance)

distances

distances(x, y)

Calculate Frobenius distances between collections of matrices.

Parameters

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

Returns

Union[List[float], IVector, IMatrix] Matrix of distances between matrices in x and y

Raises

ValueError If inputs are incompatible TypeError If input types are not supported

Source code in swarmauri_standard/metrics/FrobeniusMetric.py

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

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

    Returns
    -------
    Union[List[float], IVector, IMatrix]
        Matrix of distances between matrices in x and y

    Raises
    ------
    ValueError
        If inputs are incompatible
    TypeError
        If input types are not supported
    """
    logger.debug("Calculating Frobenius distances between collections of matrices")

    if not isinstance(x, (list, tuple, Sequence)) or not isinstance(
        y, (list, tuple, Sequence)
    ):
        raise TypeError("Both inputs must be collections of matrices")

    # Calculate pairwise distances
    distance_matrix = []
    for x_i in x:
        row = []
        for y_j in y:
            try:
                dist = self.distance(x_i, y_j)
                row.append(dist)
            except (ValueError, TypeError) as e:
                logger.warning(f"Error calculating distance: {e}")
                row.append(float("nan"))
        distance_matrix.append(row)

    return distance_matrix

check_non_negativity

check_non_negativity(x, y)

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

The Frobenius metric always satisfies this axiom by definition.

Parameters

x : MetricInput First matrix y : MetricInput Second matrix

Returns

bool True if the axiom is satisfied (always true for Frobenius metric)

Source code in swarmauri_standard/metrics/FrobeniusMetric.py

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

    The Frobenius metric always satisfies this axiom by definition.

    Parameters
    ----------
    x : MetricInput
        First matrix
    y : MetricInput
        Second matrix

    Returns
    -------
    bool
        True if the axiom is satisfied (always true for Frobenius metric)
    """
    logger.debug("Checking non-negativity axiom for Frobenius metric")
    try:
        distance_value = self.distance(x, y)
        return distance_value >= 0
    except (ValueError, TypeError) as e:
        logger.error(f"Error checking non-negativity: {e}")
        return False

check_identity_of_indiscernibles

check_identity_of_indiscernibles(x, y)

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

Parameters

x : MetricInput First matrix y : MetricInput Second matrix

Returns

bool True if the axiom is satisfied

Source code in swarmauri_standard/metrics/FrobeniusMetric.py

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

    Parameters
    ----------
    x : MetricInput
        First matrix
    y : MetricInput
        Second matrix

    Returns
    -------
    bool
        True if the axiom is satisfied
    """
    logger.debug("Checking identity of indiscernibles axiom for Frobenius metric")
    try:
        distance_value = self.distance(x, y)

        # Convert inputs to numpy arrays for comparison
        if isinstance(x, IMatrix):
            x_array = np.array(x)
        else:
            x_array = np.array(x)

        if isinstance(y, IMatrix):
            y_array = np.array(y)
        else:
            y_array = np.array(y)

        # Check if matrices are equal
        matrices_equal = np.array_equal(x_array, y_array)

        # Check the axiom: d(x,y) = 0 iff x = y
        if (
            abs(distance_value) < 1e-10
        ):  # Using small epsilon for floating-point comparison
            return matrices_equal
        else:
            return not matrices_equal

    except (ValueError, TypeError) as e:
        logger.error(f"Error checking identity of indiscernibles: {e}")
        return False

check_symmetry

check_symmetry(x, y)

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

Parameters

x : MetricInput First matrix y : MetricInput Second matrix

Returns

bool True if the axiom is satisfied

Source code in swarmauri_standard/metrics/FrobeniusMetric.py

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

    Parameters
    ----------
    x : MetricInput
        First matrix
    y : MetricInput
        Second matrix

    Returns
    -------
    bool
        True if the axiom is satisfied
    """
    logger.debug("Checking symmetry axiom for Frobenius metric")
    try:
        distance_xy = self.distance(x, y)
        distance_yx = self.distance(y, x)

        # Check if distances are equal (within floating-point precision)
        return abs(distance_xy - distance_yx) < 1e-10

    except (ValueError, TypeError) as e:
        logger.error(f"Error checking symmetry: {e}")
        return False

check_triangle_inequality

check_triangle_inequality(x, y, z)

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

Parameters

x : MetricInput First matrix y : MetricInput Second matrix z : MetricInput Third matrix

Returns

bool True if the axiom is satisfied

Source code in swarmauri_standard/metrics/FrobeniusMetric.py

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

    Parameters
    ----------
    x : MetricInput
        First matrix
    y : MetricInput
        Second matrix
    z : MetricInput
        Third matrix

    Returns
    -------
    bool
        True if the axiom is satisfied
    """
    logger.debug("Checking triangle inequality axiom for Frobenius metric")
    try:
        distance_xz = self.distance(x, z)
        distance_xy = self.distance(x, y)
        distance_yz = self.distance(y, z)

        # Check triangle inequality: d(x,z) ≤ d(x,y) + d(y,z)
        # Add small epsilon for floating-point comparison
        return distance_xz <= distance_xy + distance_yz + 1e-10

    except (ValueError, TypeError) as e:
        logger.error(f"Error checking triangle inequality: {e}")
        return False

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