Class swarmauri_standard.metrics.SobolevMetric.SobolevMetric

swarmauri_standard.metrics.SobolevMetric.SobolevMetric

SobolevMetric(**kwargs)

Bases: MetricBase

A metric derived from the Sobolev norm.

This metric accounts for both the differences in function values and their derivatives, making it suitable for measuring distance between functions where smoothness is important.

Attributes

type : Literal["SobolevMetric"] The type identifier for this metric. order : int The highest derivative order to consider in the metric computation. weights : Dict[int, float] Weights for each derivative order in the metric computation.

Initialize the Sobolev metric with specified parameters.

Parameters

**kwargs Keyword arguments to pass to the parent class constructor. May include 'order' and 'weights' to customize the metric.

Source code in swarmauri_standard/metrics/SobolevMetric.py

def __init__(self, **kwargs):
    """
    Initialize the Sobolev metric with specified parameters.

    Parameters
    ----------
    **kwargs
        Keyword arguments to pass to the parent class constructor.
        May include 'order' and 'weights' to customize the metric.
    """
    super().__init__(**kwargs)
    # Create a SobolevNorm instance to handle the norm calculations
    self.norm = SobolevNorm(order=self.order, weights=self.weights)
    logger.debug(
        f"Initialized SobolevMetric with order {self.order} and weights {self.weights}"
    )

type class-attribute instance-attribute

type = 'SobolevMetric'

order class-attribute instance-attribute

order = Field(
    default=1,
    description="Highest derivative order to consider",
)

weights class-attribute instance-attribute

weights = Field(
    default_factory=lambda: {0: 1.0, 1: 1.0},
    description="Weights for each derivative order",
)

norm instance-attribute

norm = SobolevNorm(order=order, weights=weights)

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 Sobolev distance between two functions or vectors.

The Sobolev distance is defined as the Sobolev norm of the difference between the two inputs, taking into account both values and derivatives.

Parameters

x : MetricInput First input (function or vector) y : MetricInput Second input (function or vector)

Returns

float The Sobolev distance between x and y

Raises

ValueError If inputs are incompatible or the distance cannot be computed TypeError If input types are not supported

Source code in swarmauri_standard/metrics/SobolevMetric.py

def distance(self, x: MetricInput, y: MetricInput) -> float:
    """
    Calculate the Sobolev distance between two functions or vectors.

    The Sobolev distance is defined as the Sobolev norm of the difference
    between the two inputs, taking into account both values and derivatives.

    Parameters
    ----------
    x : MetricInput
        First input (function or vector)
    y : MetricInput
        Second input (function or vector)

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

    Raises
    ------
    ValueError
        If inputs are incompatible or the distance cannot be computed
    TypeError
        If input types are not supported
    """
    logger.debug(
        f"Calculating Sobolev distance between {type(x).__name__} and {type(y).__name__}"
    )

    try:
        # Ensure x and y are of the same type
        if not isinstance(y, type(x)) and not (callable(x) and callable(y)):
            raise TypeError(
                f"Inputs must be of the same type, got {type(x).__name__} and {type(y).__name__}"
            )

        # For callable functions
        if callable(x) and callable(y):
            # Create a new function representing x - y
            def diff_func(t):
                return x(t) - y(t)

            # For functions with derivatives
            if hasattr(x, "derivative") and hasattr(y, "derivative"):
                # Add derivative method to diff_func
                def create_derivative(func_x, func_y):
                    def derivative_func(t):
                        return func_x.derivative()(t) - func_y.derivative()(t)

                    return derivative_func

                diff_func.derivative = lambda: create_derivative(x, y)

            return self.norm.compute(diff_func)

        # For vector-like objects
        elif isinstance(x, IVector) and isinstance(y, IVector):
            from swarmauri_standard.vectors.Vector import Vector

            x_array = x.to_numpy()
            y_array = y.to_numpy()
            diff_values = [x_array[i] - y_array[i] for i in range(len(x_array))]
            diff_vector = Vector(value=diff_values)
            return self.norm.compute(diff_vector)

        # For sequences
        elif isinstance(x, Sequence) and isinstance(y, Sequence):
            if len(x) != len(y):
                raise ValueError(
                    "Sequences must have the same length for distance calculation"
                )
            diff_xy = [x[i] - y[i] for i in range(len(x))]
            return self.norm.compute(diff_xy)

        else:
            raise TypeError(
                f"Cannot compute difference for type {type(x).__name__}"
            )
    except TypeError as e:
        # Re-raise TypeError directly
        logger.error(f"Error calculating Sobolev distance: {str(e)}")
        raise
    except Exception as e:
        logger.error(f"Error calculating Sobolev distance: {str(e)}")
        raise ValueError(f"Failed to calculate Sobolev distance: {str(e)}")

distances

distances(x, y)

Calculate Sobolev distances between collections of functions or vectors.

Parameters

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

Returns

Union[List[float], IVector, IMatrix] Matrix or vector of Sobolev distances between inputs in x and y

Raises

ValueError If inputs are incompatible or distances cannot be computed TypeError If input types are not supported

Source code in swarmauri_standard/metrics/SobolevMetric.py

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

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

    Returns
    -------
    Union[List[float], IVector, IMatrix]
        Matrix or vector of Sobolev distances between inputs in x and y

    Raises
    ------
    ValueError
        If inputs are incompatible or distances cannot be computed
    TypeError
        If input types are not supported
    """
    logger.debug("Calculating Sobolev distances between collections")

    try:
        # Handle different collection types
        if isinstance(x, IMatrix) and isinstance(y, IMatrix):
            # Return distance matrix between rows of x and y
            result = x.zeros((x.shape[0], y.shape[0]))
            for i in range(x.shape[0]):
                for j in range(y.shape[0]):
                    result[i, j] = self.distance(x[i], y[j])
            return result

        elif isinstance(x, IVector) and isinstance(y, IVector):
            # Return vector of distances between corresponding elements
            if x.shape[0] != y.shape[0]:
                raise ValueError(
                    "Vectors must have the same length for element-wise distances"
                )
            result = x.zeros(x.shape[0])
            for i in range(x.shape[0]):
                result[i] = self.distance(x[i], y[i])
            return result

        elif isinstance(x, list) and isinstance(y, list):
            # Return a distance matrix even for same-length lists if they contain Vector objects
            if len(x) > 0 and isinstance(x[0], IVector):
                result = [[0.0 for _ in range(len(y))] for _ in range(len(x))]
                for i in range(len(x)):
                    for j in range(len(y)):
                        result[i][j] = self.distance(x[i], y[j])
                return result
            elif len(x) != len(y):
                # Return distance matrix for different length lists
                result = [[0.0 for _ in range(len(y))] for _ in range(len(x))]
                for i in range(len(x)):
                    for j in range(len(y)):
                        result[i][j] = self.distance(x[i], y[j])
                return result
            else:
                # Return list of distances for same-length lists of non-Vector objects
                return [self.distance(x[i], y[i]) for i in range(len(x))]

        elif hasattr(x, "shape") and hasattr(y, "shape") and hasattr(x, "zeros"):
            # Handle matrix-like objects, including mocks
            result = x.zeros((x.shape[0], y.shape[0]))
            for i in range(x.shape[0]):
                for j in range(y.shape[0]):
                    result[i, j] = self.distance(x[i], y[j])
            return result

        else:
            raise TypeError(
                f"Unsupported collection types: {type(x).__name__} and {type(y).__name__}"
            )

    except Exception as e:
        logger.error(f"Error calculating Sobolev distances: {str(e)}")
        raise ValueError(f"Failed to calculate Sobolev distances: {str(e)}")

check_non_negativity

check_non_negativity(x, y)

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

Parameters

x : MetricInput First input y : MetricInput Second input

Returns

bool True if the axiom is satisfied, False otherwise

Source code in swarmauri_standard/metrics/SobolevMetric.py

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

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

    Returns
    -------
    bool
        True if the axiom is satisfied, False otherwise
    """
    logger.debug("Checking non-negativity axiom for Sobolev metric")
    try:
        dist = self.distance(x, y)
        return dist >= 0
    except Exception as e:
        logger.error(f"Error checking non-negativity: {str(e)}")
        return False

check_identity_of_indiscernibles

check_identity_of_indiscernibles(x, y)

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

Parameters

x : MetricInput First input y : MetricInput Second input

Returns

bool True if the axiom is satisfied, False otherwise

Source code in swarmauri_standard/metrics/SobolevMetric.py

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

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

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

        # Check if distance is 0
        if abs(dist) < 1e-10:
            # If distance is 0, check if x and y are effectively equal
            return self._are_effectively_equal(x, y)
        else:
            # If distance is not 0, x and y should be different
            return not self._are_effectively_equal(x, y)
    except Exception as e:
        logger.error(f"Error checking identity of indiscernibles: {str(e)}")
        return False

check_symmetry

check_symmetry(x, y)

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

Parameters

x : MetricInput First input y : MetricInput Second input

Returns

bool True if the axiom is satisfied, False otherwise

Source code in swarmauri_standard/metrics/SobolevMetric.py

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

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

    Returns
    -------
    bool
        True if the axiom is satisfied, False otherwise
    """
    logger.debug("Checking symmetry axiom for Sobolev metric")
    try:
        dist_xy = self.distance(x, y)
        dist_yx = self.distance(y, x)
        # Allow for small numerical differences
        return abs(dist_xy - dist_yx) < 1e-3 * (1 + abs(dist_xy))
    except Exception as e:
        logger.error(f"Error checking symmetry: {str(e)}")
        return False

check_triangle_inequality

check_triangle_inequality(x, y, z)

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

Parameters

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

Returns

bool True if the axiom is satisfied, False otherwise

Source code in swarmauri_standard/metrics/SobolevMetric.py

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

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

    Returns
    -------
    bool
        True if the axiom is satisfied, False otherwise
    """
    logger.debug("Checking triangle inequality axiom for Sobolev metric")
    try:
        dist_xy = self.distance(x, y)
        dist_yz = self.distance(y, z)
        dist_xz = self.distance(x, z)

        # Check the triangle inequality with a small tolerance for numerical issues
        return dist_xz <= dist_xy + dist_yz + 1e-10 * (1 + dist_xy + dist_yz)
    except Exception as e:
        logger.error(f"Error checking triangle inequality: {str(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