Class swarmauri_standard.metrics.DiscreteMetric.DiscreteMetric

swarmauri_standard.metrics.DiscreteMetric.DiscreteMetric

Bases: MetricBase

Discrete metric implementation.

This metric returns 1 if two points are different and 0 if they are the same. It works with any hashable types and satisfies all metric axioms: - Non-negativity - Identity of indiscernibles - Symmetry - Triangle inequality

The discrete metric is also known as the "trivial metric" or "0-1 metric".

type class-attribute instance-attribute

type = 'DiscreteMetric'

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 distance between two points: 1 if different, 0 if same.

Parameters

x : MetricInput First point (must be hashable) y : MetricInput Second point (must be hashable)

Returns

float 0.0 if x equals y, 1.0 otherwise

Raises

TypeError If inputs are not hashable

Source code in swarmauri_standard/metrics/DiscreteMetric.py

def distance(self, x: MetricInput, y: MetricInput) -> float:
    """
    Calculate the distance between two points: 1 if different, 0 if same.

    Parameters
    ----------
    x : MetricInput
        First point (must be hashable)
    y : MetricInput
        Second point (must be hashable)

    Returns
    -------
    float
        0.0 if x equals y, 1.0 otherwise

    Raises
    ------
    TypeError
        If inputs are not hashable
    """
    logger.debug(f"Calculating discrete distance between {x} and {y}")

    # Check if inputs are hashable
    try:
        hash(x)
        hash(y)
    except TypeError:
        error_msg = f"Inputs must be hashable, got {type(x)} and {type(y)}"
        logger.error(error_msg)
        raise TypeError(error_msg)

    # Return 0 if equal, 1 otherwise
    return 0.0 if x == y else 1.0

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 of distances between points in x and y

Raises

TypeError If inputs are not iterable or contain non-hashable elements

Source code in swarmauri_standard/metrics/DiscreteMetric.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 of distances between points in x and y

    Raises
    ------
    TypeError
        If inputs are not iterable or contain non-hashable elements
    """
    logger.debug("Calculating discrete distances between collections")

    try:
        # Convert inputs to lists if they're not already
        x_list = list(x) if not isinstance(x, (int, float)) else [x]
        y_list = list(y) if not isinstance(y, (int, float)) else [y]

        # Create a distance matrix
        result = np.zeros((len(x_list), len(y_list)))

        # Fill the distance matrix
        for i, x_val in enumerate(x_list):
            for j, y_val in enumerate(y_list):
                result[i, j] = self.distance(x_val, y_val)

        return result

    except TypeError as e:
        error_msg = f"Error calculating distances: {str(e)}"
        logger.error(error_msg)
        raise TypeError(error_msg)

check_non_negativity

check_non_negativity(x, y)

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

Always returns True for DiscreteMetric as distances are either 0 or 1.

Parameters

x : MetricInput First point y : MetricInput Second point

Returns

bool True (always satisfied for discrete metric)

Source code in swarmauri_standard/metrics/DiscreteMetric.py

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

    Always returns True for DiscreteMetric as distances are either 0 or 1.

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

    Returns
    -------
    bool
        True (always satisfied for discrete metric)
    """
    logger.debug(f"Checking non-negativity axiom for {x} and {y}")
    # Distance is always 0 or 1, so always non-negative
    return True

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.

Always returns True for DiscreteMetric by definition.

Parameters

x : MetricInput First point y : MetricInput Second point

Returns

bool True (always satisfied for discrete metric)

Source code in swarmauri_standard/metrics/DiscreteMetric.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.

    Always returns True for DiscreteMetric by definition.

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

    Returns
    -------
    bool
        True (always satisfied for discrete metric)
    """
    logger.debug(f"Checking identity of indiscernibles axiom for {x} and {y}")
    # By definition, distance is 0 if and only if x == y
    return True

check_symmetry

check_symmetry(x, y)

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

Always returns True for DiscreteMetric as equality is symmetric.

Parameters

x : MetricInput First point y : MetricInput Second point

Returns

bool True (always satisfied for discrete metric)

Source code in swarmauri_standard/metrics/DiscreteMetric.py

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

    Always returns True for DiscreteMetric as equality is symmetric.

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

    Returns
    -------
    bool
        True (always satisfied for discrete metric)
    """
    logger.debug(f"Checking symmetry axiom for {x} and {y}")
    # x == y is symmetric, so the discrete metric is symmetric
    return True

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).

Always returns True for DiscreteMetric.

Parameters

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

Returns

bool True (always satisfied for discrete metric)

Source code in swarmauri_standard/metrics/DiscreteMetric.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).

    Always returns True for DiscreteMetric.

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

    Returns
    -------
    bool
        True (always satisfied for discrete metric)
    """
    logger.debug(f"Checking triangle inequality axiom for {x}, {y}, and {z}")

    # Calculate the distances
    d_xz = self.distance(x, z)
    d_xy = self.distance(x, y)
    d_yz = self.distance(y, z)

    # Check triangle inequality
    # For discrete metric, this is always satisfied:
    # If x == z, then d_xz = 0, which is ≤ d_xy + d_yz for any values
    # If x != z, then d_xz = 1, and either:
    #   - If x != y or y != z, then d_xy + d_yz ≥ 1
    #   - If x == y and y == z, then x == z (contradiction)
    return d_xz <= d_xy + d_yz

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