Class swarmauri_standard.pseudometrics.ProjectionPseudometricR2.ProjectionPseudometricR2

swarmauri_standard.pseudometrics.ProjectionPseudometricR2.ProjectionPseudometricR2

ProjectionPseudometricR2(projection_axis=0, **kwargs)

Bases: PseudometricBase

A pseudometric that measures distance via projection in ℝ².

This pseudometric projects points onto a specified coordinate axis (x or y) and calculates the distance between their projections. Since points with different coordinates can have the same projection, this satisfies the pseudometric properties (allowing d(x,y)=0 for x≠y).

Attributes

type : Literal["ProjectionPseudometricR2"] The type identifier for this pseudometric projection_axis : int The axis to project onto (0 for x-axis, 1 for y-axis)

Initialize the ProjectionPseudometricR2.

Parameters

projection_axis : int, optional The axis to project onto (0 for x-axis, 1 for y-axis), by default 0

Raises

ValueError If projection_axis is not 0 or 1

Source code in swarmauri_standard/pseudometrics/ProjectionPseudometricR2.py

def __init__(self, projection_axis: int = 0, **kwargs):
    """
    Initialize the ProjectionPseudometricR2.

    Parameters
    ----------
    projection_axis : int, optional
        The axis to project onto (0 for x-axis, 1 for y-axis), by default 0

    Raises
    ------
    ValueError
        If projection_axis is not 0 or 1
    """
    if projection_axis not in [0, 1]:
        logger.error(
            f"Invalid projection axis: {projection_axis}. Must be 0 (x-axis) or 1 (y-axis)."
        )
        raise ValueError("Projection axis must be 0 (x-axis) or 1 (y-axis)")

    super().__init__(**kwargs, projection_axis=projection_axis)
    logger.debug(
        f"Initialized ProjectionPseudometricR2 with projection_axis={projection_axis}"
    )

type class-attribute instance-attribute

type = 'ProjectionPseudometricR2'

projection_axis class-attribute instance-attribute

projection_axis = Field(default=0, ge=0, le=1)

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 projection pseudometric distance between two 2D points.

The distance is calculated as the absolute difference between the projected coordinates on the specified axis.

Parameters

x : Union[VectorType, MatrixType, Sequence[T], str, Callable] The first 2D point y : Union[VectorType, MatrixType, Sequence[T], str, Callable] The second 2D point

Returns

float The projection pseudometric distance between x and y

Raises

TypeError If inputs are not valid 2D point representations ValueError If inputs cannot be interpreted as 2D points

Source code in swarmauri_standard/pseudometrics/ProjectionPseudometricR2.py

def distance(
    self,
    x: Union[VectorType, MatrixType, Sequence[T], str, Callable],
    y: Union[VectorType, MatrixType, Sequence[T], str, Callable],
) -> float:
    """
    Calculate the projection pseudometric distance between two 2D points.

    The distance is calculated as the absolute difference between the projected
    coordinates on the specified axis.

    Parameters
    ----------
    x : Union[VectorType, MatrixType, Sequence[T], str, Callable]
        The first 2D point
    y : Union[VectorType, MatrixType, Sequence[T], str, Callable]
        The second 2D point

    Returns
    -------
    float
        The projection pseudometric distance between x and y

    Raises
    ------
    TypeError
        If inputs are not valid 2D point representations
    ValueError
        If inputs cannot be interpreted as 2D points
    """
    logger.debug(f"Calculating projection distance between {x} and {y}")

    try:
        # Extract coordinates from both points
        x_coords = self._validate_and_extract_coordinates(x)
        y_coords = self._validate_and_extract_coordinates(y)

        # Calculate distance along the projection axis
        return abs(x_coords[self.projection_axis] - y_coords[self.projection_axis])

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

distances

distances(xs, ys)

Calculate the pairwise projection distances between two collections of 2D points.

Parameters

xs : Sequence[Union[VectorType, MatrixType, Sequence[T], str, Callable]] The first collection of 2D points ys : Sequence[Union[VectorType, MatrixType, Sequence[T], str, Callable]] The second collection of 2D points

Returns

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

Raises

TypeError If inputs contain invalid 2D point representations ValueError If inputs cannot be interpreted as 2D points

Source code in swarmauri_standard/pseudometrics/ProjectionPseudometricR2.py

def distances(
    self,
    xs: Sequence[Union[VectorType, MatrixType, Sequence[T], str, Callable]],
    ys: Sequence[Union[VectorType, MatrixType, Sequence[T], str, Callable]],
) -> List[List[float]]:
    """
    Calculate the pairwise projection distances between two collections of 2D points.

    Parameters
    ----------
    xs : Sequence[Union[VectorType, MatrixType, Sequence[T], str, Callable]]
        The first collection of 2D points
    ys : Sequence[Union[VectorType, MatrixType, Sequence[T], str, Callable]]
        The second collection of 2D points

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

    Raises
    ------
    TypeError
        If inputs contain invalid 2D point representations
    ValueError
        If inputs cannot be interpreted as 2D points
    """
    logger.debug(
        f"Calculating pairwise projection distances between {len(xs)} and {len(ys)} points"
    )

    try:
        # Pre-extract all coordinates for efficiency
        x_points = [self._validate_and_extract_coordinates(x) for x in xs]
        y_points = [self._validate_and_extract_coordinates(y) for y in ys]

        # Calculate the distance matrix
        result = []
        for x_point in x_points:
            row = []
            for y_point in y_points:
                # Calculate distance along the projection axis
                dist = abs(
                    x_point[self.projection_axis] - y_point[self.projection_axis]
                )
                row.append(dist)
            result.append(row)

        return result

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

check_non_negativity

check_non_negativity(x, y)

Check if the projection pseudometric satisfies the non-negativity property.

Parameters

x : Union[VectorType, MatrixType, Sequence[T], str, Callable] The first 2D point y : Union[VectorType, MatrixType, Sequence[T], str, Callable] The second 2D point

Returns

bool True if d(x,y) ≥ 0, which is always the case for this pseudometric

Source code in swarmauri_standard/pseudometrics/ProjectionPseudometricR2.py

def check_non_negativity(
    self,
    x: Union[VectorType, MatrixType, Sequence[T], str, Callable],
    y: Union[VectorType, MatrixType, Sequence[T], str, Callable],
) -> bool:
    """
    Check if the projection pseudometric satisfies the non-negativity property.

    Parameters
    ----------
    x : Union[VectorType, MatrixType, Sequence[T], str, Callable]
        The first 2D point
    y : Union[VectorType, MatrixType, Sequence[T], str, Callable]
        The second 2D point

    Returns
    -------
    bool
        True if d(x,y) ≥ 0, which is always the case for this pseudometric
    """
    try:
        # Calculate the distance
        dist = self.distance(x, y)

        # Check if it's non-negative
        return dist >= 0

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

check_symmetry

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

Check if the projection pseudometric satisfies the symmetry property.

Parameters

x : Union[VectorType, MatrixType, Sequence[T], str, Callable] The first 2D point y : Union[VectorType, MatrixType, Sequence[T], str, Callable] The second 2D point 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, which is always the case for this pseudometric

Source code in swarmauri_standard/pseudometrics/ProjectionPseudometricR2.py

def check_symmetry(
    self,
    x: Union[VectorType, MatrixType, Sequence[T], str, Callable],
    y: Union[VectorType, MatrixType, Sequence[T], str, Callable],
    tolerance: float = 1e-10,
) -> bool:
    """
    Check if the projection pseudometric satisfies the symmetry property.

    Parameters
    ----------
    x : Union[VectorType, MatrixType, Sequence[T], str, Callable]
        The first 2D point
    y : Union[VectorType, MatrixType, Sequence[T], str, Callable]
        The second 2D point
    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, which is always the case for this pseudometric
    """
    try:
        # Calculate distances in both directions
        dist_xy = self.distance(x, y)
        dist_yx = self.distance(y, x)

        # Check if they're equal within tolerance
        return abs(dist_xy - dist_yx) < tolerance

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

check_triangle_inequality

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

Check if the projection pseudometric satisfies the triangle inequality.

Parameters

x : Union[VectorType, MatrixType, Sequence[T], str, Callable] The first 2D point y : Union[VectorType, MatrixType, Sequence[T], str, Callable] The second 2D point z : Union[VectorType, MatrixType, Sequence[T], str, Callable] The third 2D point 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, which is always the case for this pseudometric

Source code in swarmauri_standard/pseudometrics/ProjectionPseudometricR2.py

def check_triangle_inequality(
    self,
    x: Union[VectorType, MatrixType, Sequence[T], str, Callable],
    y: Union[VectorType, MatrixType, Sequence[T], str, Callable],
    z: Union[VectorType, MatrixType, Sequence[T], str, Callable],
    tolerance: float = 1e-10,
) -> bool:
    """
    Check if the projection pseudometric satisfies the triangle inequality.

    Parameters
    ----------
    x : Union[VectorType, MatrixType, Sequence[T], str, Callable]
        The first 2D point
    y : Union[VectorType, MatrixType, Sequence[T], str, Callable]
        The second 2D point
    z : Union[VectorType, MatrixType, Sequence[T], str, Callable]
        The third 2D point
    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, which is always the case for this pseudometric
    """
    try:
        # Calculate the three distances
        dist_xy = self.distance(x, y)
        dist_yz = self.distance(y, z)
        dist_xz = self.distance(x, z)

        # Check the triangle inequality
        return dist_xz <= dist_xy + dist_yz + tolerance

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

check_weak_identity

check_weak_identity(x, y)

Check if the projection pseudometric satisfies the weak identity property.

In a pseudometric, d(x,y) = 0 is allowed even when x ≠ y. For the projection pseudometric, this happens when two points have the same coordinate on the projection axis but differ in the other coordinate.

Parameters

x : Union[VectorType, MatrixType, Sequence[T], str, Callable] The first 2D point y : Union[VectorType, MatrixType, Sequence[T], str, Callable] The second 2D point

Returns

bool True if the pseudometric correctly handles the weak identity property

Source code in swarmauri_standard/pseudometrics/ProjectionPseudometricR2.py

def check_weak_identity(
    self,
    x: Union[VectorType, MatrixType, Sequence[T], str, Callable],
    y: Union[VectorType, MatrixType, Sequence[T], str, Callable],
) -> bool:
    """
    Check if the projection pseudometric satisfies the weak identity property.

    In a pseudometric, d(x,y) = 0 is allowed even when x ≠ y. For the projection
    pseudometric, this happens when two points have the same coordinate on the
    projection axis but differ in the other coordinate.

    Parameters
    ----------
    x : Union[VectorType, MatrixType, Sequence[T], str, Callable]
        The first 2D point
    y : Union[VectorType, MatrixType, Sequence[T], str, Callable]
        The second 2D point

    Returns
    -------
    bool
        True if the pseudometric correctly handles the weak identity property
    """
    try:
        # Extract coordinates
        x_coords = self._validate_and_extract_coordinates(x)
        y_coords = self._validate_and_extract_coordinates(y)

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

        # Check if points are different but have the same projection
        points_differ = x_coords != y_coords
        same_projection = (
            x_coords[self.projection_axis] == y_coords[self.projection_axis]
        )

        # If points differ but have same projection, distance should be 0
        if points_differ and same_projection:
            return abs(dist) < 1e-10

        # If points are the same, distance should be 0
        if not points_differ:
            return abs(dist) < 1e-10

        # If points differ and have different projections, distance should be > 0
        return dist > 0

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

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