Class swarmauri_standard.metrics.SupremumMetric.SupremumMetric

swarmauri_standard.metrics.SupremumMetric.SupremumMetric

Bases: MetricBase

L∞-based metric measuring largest component difference.

This metric computes the distance between two points as the maximum absolute difference between their corresponding components. It is also known as the Chebyshev distance or the L∞ metric.

The metric is particularly useful in bounded vector spaces where the maximum deviation between components is more important than the overall sum of differences.

Attributes

type : Literal["SupremumMetric"] The type identifier for this metric implementation. resource : str, optional The resource type, defaults to METRIC.

type class-attribute instance-attribute

type = 'SupremumMetric'

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 supremum (maximum) distance between two points.

Parameters

x : MetricInput First point y : MetricInput Second point

Returns

float The maximum absolute difference between corresponding components

Raises

ValueError If inputs have different dimensions or are incompatible TypeError If input types are not supported

Source code in swarmauri_standard/metrics/SupremumMetric.py

def distance(self, x: MetricInput, y: MetricInput) -> float:
    """
    Calculate the supremum (maximum) distance between two points.

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

    Returns
    -------
    float
        The maximum absolute difference between corresponding components

    Raises
    ------
    ValueError
        If inputs have different dimensions or are incompatible
    TypeError
        If input types are not supported
    """
    logger.debug(f"Calculating supremum distance between {x} and {y}")

    try:
        # Handle different types of inputs
        if hasattr(x, "to_array") and hasattr(y, "to_array"):
            # For vector types with to_array method
            x_array = x.to_array()
            y_array = y.to_array()
            return self._calculate_supremum(x_array, y_array)

        elif isinstance(x, (list, tuple)) and isinstance(y, (list, tuple)):
            # For sequence types
            if len(x) != len(y):
                raise ValueError(
                    f"Inputs have different dimensions: {len(x)} vs {len(y)}"
                )
            return self._calculate_supremum(x, y)

        elif isinstance(x, np.ndarray) and isinstance(y, np.ndarray):
            # For numpy arrays
            if x.shape != y.shape:
                raise ValueError(
                    f"Inputs have different shapes: {x.shape} vs {y.shape}"
                )
            return self._calculate_supremum(x, y)

        elif isinstance(x, (int, float)) and isinstance(y, (int, float)):
            # For scalar values
            return abs(x - y)

        else:
            # Try to handle other types
            try:
                return abs(x - y)
            except TypeError:
                raise TypeError(f"Unsupported input types: {type(x)} and {type(y)}")

    except Exception as e:
        logger.error(f"Error calculating supremum distance: {str(e)}")
        raise

distances

distances(x, y)

Calculate distances between collections of points.

Parameters

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

Returns

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

Raises

ValueError If inputs are incompatible with the metric TypeError If input types are not supported

Source code in swarmauri_standard/metrics/SupremumMetric.py

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

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

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

    Raises
    ------
    ValueError
        If inputs are incompatible with the metric
    TypeError
        If input types are not supported
    """
    logger.debug("Calculating supremum distances between collections")

    try:
        # Case 1: Both inputs are lists/sequences of points
        if isinstance(x, (list, tuple)) and isinstance(y, (list, tuple)):
            # Check if x is a list of lists (collection) or a single point
            x_is_collection = len(x) > 0 and isinstance(
                x[0], (list, tuple, np.ndarray)
            )
            # Check if y is a list of lists (collection) or a single point
            y_is_collection = len(y) > 0 and isinstance(
                y[0], (list, tuple, np.ndarray)
            )

            # Both are collections
            if x_is_collection and y_is_collection:
                # Compute pairwise distances (distance matrix)
                return [[self.distance(xi, yj) for yj in y] for xi in x]

            # x is a collection, y is a single point
            elif x_is_collection and not y_is_collection:
                # Compute distances from each point in x to y
                return [self.distance(xi, y) for xi in x]

            # x is a single point, y is a collection
            elif not x_is_collection and y_is_collection:
                # Compute distances from x to each point in y
                return [self.distance(x, yi) for yi in y]

            # Both are single points
            else:
                # Compute distance between corresponding points if lists have same length
                if len(x) != len(y):
                    raise ValueError(
                        f"Collections have different lengths: {len(x)} vs {len(y)}"
                    )
                return [self.distance(xi, yi) for xi, yi in zip(x, y)]
        # Case 2: x is a single point, y is a collection
        elif not isinstance(x, (list, tuple)) and isinstance(y, (list, tuple)):
            return [self.distance(x, yi) for yi in y]

        # Case 3: x is a collection, y is a single point
        elif isinstance(x, (list, tuple)) and not isinstance(y, (list, tuple)):
            return [self.distance(xi, y) for xi in x]

        # Case 4: Both are matrices or vectors with special methods
        elif hasattr(x, "shape") and hasattr(y, "shape"):
            # For matrix or vector types with shape attribute
            if x.shape == y.shape:
                # Element-wise distances
                if hasattr(x, "to_array") and hasattr(y, "to_array"):
                    x_array = x.to_array()
                    y_array = y.to_array()
                    return self._calculate_supremum(x_array, y_array)
            else:
                # Create a distance matrix
                result = []
                for i in range(x.shape[0]):
                    row = []
                    for j in range(y.shape[0]):
                        if hasattr(x, "get_row") and hasattr(y, "get_row"):
                            row.append(self.distance(x.get_row(i), y.get_row(j)))
                        else:
                            raise TypeError(
                                "Objects have shape but no get_row method"
                            )
                    result.append(row)
                return result

        else:
            # Case 5: Single distance calculation
            return self.distance(x, y)

    except Exception as e:
        logger.error(f"Error calculating supremum distances: {str(e)}")
        raise

check_non_negativity

check_non_negativity(x, y)

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

The supremum metric always satisfies this axiom as it's based on absolute differences.

Parameters

x : MetricInput First point y : MetricInput Second point

Returns

bool True if the axiom is satisfied, False otherwise

Source code in swarmauri_standard/metrics/SupremumMetric.py

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

    The supremum metric always satisfies this axiom as it's based on absolute differences.

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

    Returns
    -------
    bool
        True if the axiom is satisfied, False otherwise
    """
    logger.debug("Checking non-negativity axiom for supremum metric")

    try:
        # Calculate the distance
        dist = self.distance(x, y)

        # Check if the distance is non-negative
        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 metric satisfies the identity of indiscernibles axiom: d(x,y) = 0 if and only if x = y.

Parameters

x : MetricInput First point y : MetricInput Second point

Returns

bool True if the axiom is satisfied, False otherwise

Source code in swarmauri_standard/metrics/SupremumMetric.py

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

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

    Returns
    -------
    bool
        True if the axiom is satisfied, False otherwise
    """
    logger.debug("Checking identity of indiscernibles axiom for supremum metric")

    try:
        # Calculate the distance
        dist = self.distance(x, y)

        # Check if the points are equal
        equal = False

        if hasattr(x, "to_array") and hasattr(y, "to_array"):
            x_array = x.to_array()
            y_array = y.to_array()
            equal = np.array_equal(x_array, y_array)
        elif isinstance(x, (list, tuple)) and isinstance(y, (list, tuple)):
            equal = x == y
        elif isinstance(x, np.ndarray) and isinstance(y, np.ndarray):
            equal = np.array_equal(x, y)
        else:
            equal = x == y

        # Check if the axiom is satisfied
        # Distance is 0 iff the points are equal
        return (dist == 0) == equal
    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 metric satisfies the symmetry axiom: d(x,y) = d(y,x).

The supremum metric always satisfies this axiom as absolute differences are symmetric.

Parameters

x : MetricInput First point y : MetricInput Second point

Returns

bool True if the axiom is satisfied, False otherwise

Source code in swarmauri_standard/metrics/SupremumMetric.py

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

    The supremum metric always satisfies this axiom as absolute differences are symmetric.

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

    Returns
    -------
    bool
        True if the axiom is satisfied, False otherwise
    """
    logger.debug("Checking symmetry axiom for supremum metric")

    try:
        # Calculate distances in both directions
        dist_xy = self.distance(x, y)
        dist_yx = self.distance(y, x)

        # Check if the distances are equal (within a small epsilon for floating-point comparison)
        epsilon = 1e-10
        return abs(dist_xy - dist_yx) < epsilon
    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 metric satisfies the triangle inequality axiom: d(x,z) ≤ d(x,y) + d(y,z).

Parameters

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

Returns

bool True if the axiom is satisfied, False otherwise

Source code in swarmauri_standard/metrics/SupremumMetric.py

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

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

    Returns
    -------
    bool
        True if the axiom is satisfied, False otherwise
    """
    logger.debug("Checking triangle inequality axiom for supremum metric")

    try:
        # Calculate the three distances
        dist_xy = self.distance(x, y)
        dist_yz = self.distance(y, z)
        dist_xz = self.distance(x, z)

        # Check if the triangle inequality holds
        # Use a small epsilon for floating-point comparison
        epsilon = 1e-10
        return dist_xz <= dist_xy + dist_yz + epsilon
    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