Class swarmauri_standard.pseudometrics.FunctionDifferencePseudometric.FunctionDifferencePseudometric

swarmauri_standard.pseudometrics.FunctionDifferencePseudometric.FunctionDifferencePseudometric

FunctionDifferencePseudometric(
    evaluation_points=None,
    num_samples=10,
    sampling_strategy="fixed",
    domain_bounds=None,
    norm_type="l2",
)

Bases: PseudometricBase

Measures the distance between two functions based on their output differences.

This pseudometric calculates the distance between functions by evaluating them at specific points and measuring the differences in their outputs. Functions are considered close if they produce similar outputs at the evaluation points, even if they differ elsewhere.

Attributes

type : Literal["FunctionDifferencePseudometric"] The type identifier for this pseudometric. evaluation_points : Optional[List[Any]] The specific points at which to evaluate the functions. num_samples : int Number of points to sample if using random sampling. sampling_strategy : str Strategy for sampling points ('fixed', 'random', 'grid'). domain_bounds : Optional[Dict[str, Tuple[float, float]]] Bounds for the domain when using random or grid sampling. norm_type : str The type of norm to use for calculating differences ('l1', 'l2', 'max').

Initialize the FunctionDifferencePseudometric.

Parameters

evaluation_points : Optional[List[Any]], optional The specific points at which to evaluate the functions. Required if sampling_strategy is 'fixed'. num_samples : int, optional Number of points to sample if using random sampling, by default 10. sampling_strategy : str, optional Strategy for sampling points ('fixed', 'random', 'grid'), by default "fixed". domain_bounds : Optional[Dict[str, tuple]], optional Bounds for the domain when using random or grid sampling, by default None. Example: {'x': (-1, 1), 'y': (0, 2)} for a 2D domain. norm_type : str, optional The type of norm to use for calculating differences ('l1', 'l2', 'max'), by default "l2".

Raises

ValueError If evaluation_points is None and sampling_strategy is 'fixed', or if domain_bounds is None and sampling_strategy is 'random' or 'grid'.

Source code in swarmauri_standard/pseudometrics/FunctionDifferencePseudometric.py

def __init__(
    self,
    evaluation_points: Optional[List[Any]] = None,
    num_samples: int = 10,
    sampling_strategy: str = "fixed",
    domain_bounds: Optional[Dict[str, tuple]] = None,
    norm_type: str = "l2",
):
    """
    Initialize the FunctionDifferencePseudometric.

    Parameters
    ----------
    evaluation_points : Optional[List[Any]], optional
        The specific points at which to evaluate the functions. Required if
        sampling_strategy is 'fixed'.
    num_samples : int, optional
        Number of points to sample if using random sampling, by default 10.
    sampling_strategy : str, optional
        Strategy for sampling points ('fixed', 'random', 'grid'), by default "fixed".
    domain_bounds : Optional[Dict[str, tuple]], optional
        Bounds for the domain when using random or grid sampling, by default None.
        Example: {'x': (-1, 1), 'y': (0, 2)} for a 2D domain.
    norm_type : str, optional
        The type of norm to use for calculating differences ('l1', 'l2', 'max'),
        by default "l2".

    Raises
    ------
    ValueError
        If evaluation_points is None and sampling_strategy is 'fixed',
        or if domain_bounds is None and sampling_strategy is 'random' or 'grid'.
    """
    super().__init__()

    # Validate inputs
    if sampling_strategy == "fixed" and evaluation_points is None:
        raise ValueError(
            "evaluation_points must be provided when sampling_strategy is 'fixed'"
        )

    if sampling_strategy in ["random", "grid"] and domain_bounds is None:
        raise ValueError(
            "domain_bounds must be provided when sampling_strategy is 'random' or 'grid'"
        )

    if norm_type not in ["l1", "l2", "max"]:
        raise ValueError("norm_type must be one of 'l1', 'l2', or 'max'")

    self.evaluation_points = evaluation_points
    self.num_samples = num_samples
    self.sampling_strategy = sampling_strategy
    self.domain_bounds = domain_bounds
    self.norm_type = norm_type

    # Generate sample points if not fixed
    self._sample_points = None
    if sampling_strategy != "fixed":
        self._generate_sample_points()

    logger.debug(
        f"Initialized FunctionDifferencePseudometric with strategy={sampling_strategy}, "
        f"num_samples={num_samples}, norm_type={norm_type}"
    )

type class-attribute instance-attribute

type = 'FunctionDifferencePseudometric'

evaluation_points instance-attribute

evaluation_points = evaluation_points

num_samples instance-attribute

num_samples = num_samples

sampling_strategy instance-attribute

sampling_strategy = sampling_strategy

domain_bounds instance-attribute

domain_bounds = domain_bounds

norm_type instance-attribute

norm_type = norm_type

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 between two functions.

Parameters

x : Callable The first function y : Callable The second function

Returns

float The distance between the functions based on their output differences

Raises

TypeError If inputs are not callable ValueError If functions cannot be evaluated at the sample points

Source code in swarmauri_standard/pseudometrics/FunctionDifferencePseudometric.py

def distance(self, x: Callable, y: Callable) -> float:
    """
    Calculate the pseudometric distance between two functions.

    Parameters
    ----------
    x : Callable
        The first function
    y : Callable
        The second function

    Returns
    -------
    float
        The distance between the functions based on their output differences

    Raises
    ------
    TypeError
        If inputs are not callable
    ValueError
        If functions cannot be evaluated at the sample points
    """
    # Validate inputs
    if not callable(x) or not callable(y):
        logger.error("Both inputs must be callable functions")
        raise TypeError("Both inputs must be callable functions")

    try:
        # Get evaluation points
        points = self._get_evaluation_points()

        # Evaluate functions at the points
        values_x = self._evaluate_function(x, points)
        values_y = self._evaluate_function(y, points)

        # Calculate difference
        diff = self._calculate_difference(values_x, values_y)

        logger.debug(f"Function difference distance: {diff}")
        return diff

    except Exception as e:
        logger.error(f"Error calculating function difference: {e}")
        raise

distances

distances(xs, ys)

Calculate the pairwise distances between two collections of functions.

Parameters

xs : Sequence[Callable] The first collection of functions ys : Sequence[Callable] The second collection of functions

Returns

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

Raises

TypeError If any input is not callable ValueError If functions cannot be evaluated at the sample points

Source code in swarmauri_standard/pseudometrics/FunctionDifferencePseudometric.py

def distances(
    self, xs: Sequence[Callable], ys: Sequence[Callable]
) -> List[List[float]]:
    """
    Calculate the pairwise distances between two collections of functions.

    Parameters
    ----------
    xs : Sequence[Callable]
        The first collection of functions
    ys : Sequence[Callable]
        The second collection of functions

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

    Raises
    ------
    TypeError
        If any input is not callable
    ValueError
        If functions cannot be evaluated at the sample points
    """
    # Validate inputs
    if not all(callable(f) for f in xs) or not all(callable(f) for f in ys):
        logger.error("All inputs must be callable functions")
        raise TypeError("All inputs must be callable functions")

    try:
        # Get evaluation points
        points = self._get_evaluation_points()

        # Pre-compute function values to avoid redundant evaluations
        values_xs = [self._evaluate_function(f, points) for f in xs]
        values_ys = [self._evaluate_function(f, points) for f in ys]

        # Calculate all pairwise distances
        result = []
        for values_x in values_xs:
            row = []
            for values_y in values_ys:
                diff = self._calculate_difference(values_x, values_y)
                row.append(diff)
            result.append(row)

        return result

    except Exception as e:
        logger.error(f"Error calculating pairwise function differences: {e}")
        raise

check_non_negativity

check_non_negativity(x, y)

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

For a pseudometric, d(x,y) ≥ 0 must always hold.

Parameters

x : Callable The first function y : Callable The second function

Returns

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

Source code in swarmauri_standard/pseudometrics/FunctionDifferencePseudometric.py

def check_non_negativity(self, x: Callable, y: Callable) -> bool:
    """
    Check if the distance function satisfies the non-negativity property.

    For a pseudometric, d(x,y) ≥ 0 must always hold.

    Parameters
    ----------
    x : Callable
        The first function
    y : Callable
        The second function

    Returns
    -------
    bool
        True if d(x,y) ≥ 0, False otherwise
    """
    try:
        dist = self.distance(x, y)
        result = dist >= 0

        if not result:
            logger.warning(f"Non-negativity check failed: distance = {dist}")

        return result

    except Exception as e:
        logger.error(f"Error checking non-negativity: {e}")
        raise

check_symmetry

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

Check if the distance function satisfies the symmetry property.

For a pseudometric, d(x,y) = d(y,x) must hold.

Parameters

x : Callable The first function y : Callable The second function 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/FunctionDifferencePseudometric.py

def check_symmetry(
    self, x: Callable, y: Callable, tolerance: float = 1e-10
) -> bool:
    """
    Check if the distance function satisfies the symmetry property.

    For a pseudometric, d(x,y) = d(y,x) must hold.

    Parameters
    ----------
    x : Callable
        The first function
    y : Callable
        The second function
    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:
        dist_xy = self.distance(x, y)
        dist_yx = self.distance(y, x)

        result = abs(dist_xy - dist_yx) <= tolerance

        if not result:
            logger.warning(
                f"Symmetry check failed: d(x,y) = {dist_xy}, d(y,x) = {dist_yx}"
            )

        return result

    except Exception as e:
        logger.error(f"Error checking symmetry: {e}")
        raise

check_triangle_inequality

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

Check if the distance function satisfies the triangle inequality.

For a pseudometric, d(x,z) ≤ d(x,y) + d(y,z) must hold.

Parameters

x : Callable The first function y : Callable The second function z : Callable The third function 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/FunctionDifferencePseudometric.py

def check_triangle_inequality(
    self, x: Callable, y: Callable, z: Callable, tolerance: float = 1e-10
) -> bool:
    """
    Check if the distance function satisfies the triangle inequality.

    For a pseudometric, d(x,z) ≤ d(x,y) + d(y,z) must hold.

    Parameters
    ----------
    x : Callable
        The first function
    y : Callable
        The second function
    z : Callable
        The third function
    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:
        dist_xy = self.distance(x, y)
        dist_yz = self.distance(y, z)
        dist_xz = self.distance(x, z)

        result = dist_xz <= dist_xy + dist_yz + tolerance

        if not result:
            logger.warning(
                f"Triangle inequality check failed: "
                f"d(x,z) = {dist_xz}, d(x,y) + d(y,z) = {dist_xy + dist_yz}"
            )

        return result

    except Exception as e:
        logger.error(f"Error checking triangle inequality: {e}")
        raise

check_weak_identity

check_weak_identity(x, y)

Check if the distance function satisfies the weak identity property.

For a pseudometric, d(x,y) = 0 is allowed even when x ≠ y, which happens when the functions produce identical outputs at all evaluation points.

Parameters

x : Callable The first function y : Callable The second function

Returns

bool True if the pseudometric properly handles the weak identity property

Source code in swarmauri_standard/pseudometrics/FunctionDifferencePseudometric.py

def check_weak_identity(self, x: Callable, y: Callable) -> bool:
    """
    Check if the distance function satisfies the weak identity property.

    For a pseudometric, d(x,y) = 0 is allowed even when x ≠ y, which happens
    when the functions produce identical outputs at all evaluation points.

    Parameters
    ----------
    x : Callable
        The first function
    y : Callable
        The second function

    Returns
    -------
    bool
        True if the pseudometric properly handles the weak identity property
    """
    try:
        # Create a function that's equal to x at all evaluation points but different elsewhere
        points = self._get_evaluation_points()

        # Evaluate x at all points to create a lookup table
        x_values = {}
        for point in points:
            x_values[str(point)] = x(point)

        # Create a function that matches x at evaluation points but differs elsewhere
        def modified_x(p):
            # If p is in our evaluation points, return the same value as x
            p_str = str(p)
            if p_str in x_values:
                return x_values[p_str]
            # Otherwise, return a different value
            return x(p) + 1.0 if callable(x) else 1.0

        # The distance between x and modified_x should be 0
        # (they're equal at all evaluation points)
        dist = self.distance(x, modified_x)

        result = abs(dist) <= 1e-10

        if not result:
            logger.warning(f"Weak identity check failed: distance = {dist}")

        return result

    except Exception as e:
        logger.error(f"Error checking weak identity: {e}")
        raise

to_dict

to_dict()

Convert the pseudometric to a dictionary representation.

Returns

Dict[str, Any] Dictionary representation of the pseudometric

Source code in swarmauri_standard/pseudometrics/FunctionDifferencePseudometric.py

def to_dict(self) -> Dict[str, Any]:
    """
    Convert the pseudometric to a dictionary representation.

    Returns
    -------
    Dict[str, Any]
        Dictionary representation of the pseudometric
    """
    return {
        "type": self.type,
        "evaluation_points": self.evaluation_points,
        "num_samples": self.num_samples,
        "sampling_strategy": self.sampling_strategy,
        "domain_bounds": self.domain_bounds,
        "norm_type": self.norm_type,
    }

from_dict classmethod

from_dict(data)

Create a FunctionDifferencePseudometric from a dictionary representation.

Parameters

data : Dict[str, Any] Dictionary representation of the pseudometric

Returns

FunctionDifferencePseudometric The reconstructed pseudometric

Source code in swarmauri_standard/pseudometrics/FunctionDifferencePseudometric.py

@classmethod
def from_dict(cls, data: Dict[str, Any]) -> "FunctionDifferencePseudometric":
    """
    Create a FunctionDifferencePseudometric from a dictionary representation.

    Parameters
    ----------
    data : Dict[str, Any]
        Dictionary representation of the pseudometric

    Returns
    -------
    FunctionDifferencePseudometric
        The reconstructed pseudometric
    """
    return cls(
        evaluation_points=data.get("evaluation_points"),
        num_samples=data.get("num_samples", 10),
        sampling_strategy=data.get("sampling_strategy", "fixed"),
        domain_bounds=data.get("domain_bounds"),
        norm_type=data.get("norm_type", "l2"),
    )

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