Class swarmauri_standard.similarities.TriangleCosineSimilarity.TriangleCosineSimilarity

swarmauri_standard.similarities.TriangleCosineSimilarity.TriangleCosineSimilarity

Bases: SimilarityBase

Triangle Cosine Similarity implementation that compares direction between vectors.

This similarity measure uses a tighter bound derived from the spherical law of cosines, providing a more accurate measure of directional similarity between non-zero vectors.

Attributes

type : Literal["TriangleCosineSimilarity"] The type identifier for this similarity measure

type class-attribute instance-attribute

type = 'TriangleCosineSimilarity'

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 = SIMILARITY.value

version class-attribute instance-attribute

version = '0.1.0'

similarity

similarity(x, y)

Calculate the triangle cosine similarity between two vectors.

Uses the dot product over the product of norms, with a tighter bound derived from the spherical law of cosines.

Parameters

x : ComparableType First vector y : ComparableType Second vector

Returns

float Similarity score between x and y, in the range [0, 1]

Raises

ValueError If inputs are zero vectors or have incompatible dimensions TypeError If inputs cannot be converted to numpy arrays

Source code in swarmauri_standard/similarities/TriangleCosineSimilarity.py

def similarity(self, x: ComparableType, y: ComparableType) -> float:
    """
    Calculate the triangle cosine similarity between two vectors.

    Uses the dot product over the product of norms, with a tighter bound
    derived from the spherical law of cosines.

    Parameters
    ----------
    x : ComparableType
        First vector
    y : ComparableType
        Second vector

    Returns
    -------
    float
        Similarity score between x and y, in the range [0, 1]

    Raises
    ------
    ValueError
        If inputs are zero vectors or have incompatible dimensions
    TypeError
        If inputs cannot be converted to numpy arrays
    """
    try:
        # Validate and convert inputs
        x_array = self._validate_vector(x)
        y_array = self._validate_vector(y)

        # Check for compatible dimensions
        if x_array.shape != y_array.shape:
            raise ValueError(
                f"Incompatible dimensions: {x_array.shape} vs {y_array.shape}"
            )

        # Calculate norms
        x_norm = np.linalg.norm(x_array)
        y_norm = np.linalg.norm(y_array)

        # Calculate dot product
        dot_product = np.dot(x_array, y_array)

        # Standard cosine similarity calculation
        cosine = dot_product / (x_norm * y_norm)

        # Clamp to handle floating point errors
        cosine = max(min(cosine, 1.0), -1.0)

        # Apply triangle-based transformation for tighter bounds
        # This transforms the range [-1, 1] to [0, 1] with a non-linear mapping
        # derived from the spherical law of cosines
        angle = math.acos(cosine)
        similarity = 1.0 - (angle / math.pi)

        return similarity

    except Exception as e:
        logger.error(f"Error calculating triangle cosine similarity: {str(e)}")
        raise

similarities

similarities(x, ys)

Calculate triangle cosine similarities between one vector and multiple others.

Parameters

x : ComparableType Reference vector ys : Sequence[ComparableType] Sequence of vectors to compare against the reference

Returns

List[float] List of similarity scores between x and each element in ys

Raises

ValueError If any vectors are zero or have incompatible dimensions TypeError If any inputs cannot be converted to numpy arrays

Source code in swarmauri_standard/similarities/TriangleCosineSimilarity.py

def similarities(
    self, x: ComparableType, ys: Sequence[ComparableType]
) -> List[float]:
    """
    Calculate triangle cosine similarities between one vector and multiple others.

    Parameters
    ----------
    x : ComparableType
        Reference vector
    ys : Sequence[ComparableType]
        Sequence of vectors to compare against the reference

    Returns
    -------
    List[float]
        List of similarity scores between x and each element in ys

    Raises
    ------
    ValueError
        If any vectors are zero or have incompatible dimensions
    TypeError
        If any inputs cannot be converted to numpy arrays
    """
    try:
        # Validate reference vector once
        x_array = self._validate_vector(x)
        x_norm = np.linalg.norm(x_array)

        results = []
        for y in ys:
            try:
                y_array = self._validate_vector(y)

                # Check for compatible dimensions
                if x_array.shape != y_array.shape:
                    raise ValueError(
                        f"Incompatible dimensions: {x_array.shape} vs {y_array.shape}"
                    )

                y_norm = np.linalg.norm(y_array)
                dot_product = np.dot(x_array, y_array)

                # Standard cosine similarity calculation
                cosine = dot_product / (x_norm * y_norm)

                # Clamp to handle floating point errors
                cosine = max(min(cosine, 1.0), -1.0)

                # Apply triangle-based transformation
                angle = math.acos(cosine)
                similarity = 1.0 - (angle / math.pi)

                results.append(similarity)
            except Exception as e:
                logger.warning(
                    f"Error calculating similarity for one vector: {str(e)}"
                )
                # Append None or a default value, or re-raise based on requirements
                results.append(float("nan"))

        return results

    except Exception as e:
        logger.error(
            f"Error calculating multiple triangle cosine similarities: {str(e)}"
        )
        raise

dissimilarity

dissimilarity(x, y)

Calculate the triangle cosine dissimilarity between two vectors.

Parameters

x : ComparableType First vector y : ComparableType Second vector

Returns

float Dissimilarity score between x and y, in the range [0, 1]

Raises

ValueError If inputs are zero vectors or have incompatible dimensions TypeError If inputs cannot be converted to numpy arrays

Source code in swarmauri_standard/similarities/TriangleCosineSimilarity.py

def dissimilarity(self, x: ComparableType, y: ComparableType) -> float:
    """
    Calculate the triangle cosine dissimilarity between two vectors.

    Parameters
    ----------
    x : ComparableType
        First vector
    y : ComparableType
        Second vector

    Returns
    -------
    float
        Dissimilarity score between x and y, in the range [0, 1]

    Raises
    ------
    ValueError
        If inputs are zero vectors or have incompatible dimensions
    TypeError
        If inputs cannot be converted to numpy arrays
    """
    try:
        # Simply use 1 - similarity since our measure is bounded [0, 1]
        return 1.0 - self.similarity(x, y)
    except Exception as e:
        logger.error(f"Error calculating triangle cosine dissimilarity: {str(e)}")
        raise

check_bounded

check_bounded()

Check if the similarity measure is bounded.

The triangle cosine similarity is bounded in the range [0, 1].

Returns

bool True, as triangle cosine similarity is bounded

Source code in swarmauri_standard/similarities/TriangleCosineSimilarity.py

def check_bounded(self) -> bool:
    """
    Check if the similarity measure is bounded.

    The triangle cosine similarity is bounded in the range [0, 1].

    Returns
    -------
    bool
        True, as triangle cosine similarity is bounded
    """
    return True

check_reflexivity

check_reflexivity(x)

Check if the similarity measure is reflexive: s(x,x) = 1.

Parameters

x : ComparableType Vector to check reflexivity with

Returns

bool True if s(x,x) = 1, False otherwise

Raises

ValueError If input is a zero vector TypeError If input cannot be converted to a numpy array

Source code in swarmauri_standard/similarities/TriangleCosineSimilarity.py

def check_reflexivity(self, x: ComparableType) -> bool:
    """
    Check if the similarity measure is reflexive: s(x,x) = 1.

    Parameters
    ----------
    x : ComparableType
        Vector to check reflexivity with

    Returns
    -------
    bool
        True if s(x,x) = 1, False otherwise

    Raises
    ------
    ValueError
        If input is a zero vector
    TypeError
        If input cannot be converted to a numpy array
    """
    try:
        x_array = self._validate_vector(x)
        similarity_value = self.similarity(x_array, x_array)
        return abs(similarity_value - 1.0) < 1e-10
    except Exception as e:
        logger.error(f"Error checking reflexivity: {str(e)}")
        raise

check_symmetry

check_symmetry(x, y)

Check if the similarity measure is symmetric: s(x,y) = s(y,x).

Parameters

x : ComparableType First vector to compare y : ComparableType Second vector to compare

Returns

bool True if s(x,y) = s(y,x), False otherwise

Raises

ValueError If inputs are zero vectors or have incompatible dimensions TypeError If inputs cannot be converted to numpy arrays

Source code in swarmauri_standard/similarities/TriangleCosineSimilarity.py

def check_symmetry(self, x: ComparableType, y: ComparableType) -> bool:
    """
    Check if the similarity measure is symmetric: s(x,y) = s(y,x).

    Parameters
    ----------
    x : ComparableType
        First vector to compare
    y : ComparableType
        Second vector to compare

    Returns
    -------
    bool
        True if s(x,y) = s(y,x), False otherwise

    Raises
    ------
    ValueError
        If inputs are zero vectors or have incompatible dimensions
    TypeError
        If inputs cannot be converted to numpy arrays
    """
    try:
        # Validate inputs
        x_array = self._validate_vector(x)
        y_array = self._validate_vector(y)

        similarity_xy = self.similarity(x_array, y_array)
        similarity_yx = self.similarity(y_array, x_array)

        return abs(similarity_xy - similarity_yx) < 1e-10
    except Exception as e:
        logger.error(f"Error checking symmetry: {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

dissimilarities

dissimilarities(x, ys)

Calculate dissimilarities between one object and multiple other objects.

Parameters

x : ComparableType Reference object ys : Sequence[ComparableType] Sequence of objects to compare against the reference

Returns

List[float] List of dissimilarity scores between x and each element in ys

Raises

NotImplementedError This method must be implemented by subclasses ValueError If any objects are incomparable or have incompatible dimensions TypeError If any input types are not supported

Source code in swarmauri_base/similarities/SimilarityBase.py

def dissimilarities(
    self, x: ComparableType, ys: Sequence[ComparableType]
) -> List[float]:
    """
    Calculate dissimilarities between one object and multiple other objects.

    Parameters
    ----------
    x : ComparableType
        Reference object
    ys : Sequence[ComparableType]
        Sequence of objects to compare against the reference

    Returns
    -------
    List[float]
        List of dissimilarity scores between x and each element in ys

    Raises
    ------
    NotImplementedError
        This method must be implemented by subclasses
    ValueError
        If any objects are incomparable or have incompatible dimensions
    TypeError
        If any input types are not supported
    """
    # Default implementation can be overridden for efficiency
    try:
        return [self.dissimilarity(x, y) for y in ys]
    except Exception as e:
        logger.error(f"Error calculating dissimilarities: {str(e)}")
        raise

check_identity_of_discernibles

check_identity_of_discernibles(x, y)

Check if the similarity measure satisfies the identity of discernibles: s(x,y) = 1 ⟺ x = y.

Parameters

x : ComparableType First object to compare y : ComparableType Second object to compare

Returns

bool True if the identity of discernibles property holds, False otherwise

Raises

ValueError If the objects are incomparable or have incompatible dimensions TypeError If the input types are not supported

Source code in swarmauri_base/similarities/SimilarityBase.py

def check_identity_of_discernibles(
    self, x: ComparableType, y: ComparableType
) -> bool:
    """
    Check if the similarity measure satisfies the identity of discernibles: s(x,y) = 1 ⟺ x = y.

    Parameters
    ----------
    x : ComparableType
        First object to compare
    y : ComparableType
        Second object to compare

    Returns
    -------
    bool
        True if the identity of discernibles property holds, False otherwise

    Raises
    ------
    ValueError
        If the objects are incomparable or have incompatible dimensions
    TypeError
        If the input types are not supported
    """
    try:
        similarity_value = self.similarity(x, y)
        # If x and y are identical (by value, not necessarily by reference)
        if str(x) == str(y):  # Simple string comparison as a basic equality check
            # Then the similarity should be 1
            return abs(similarity_value - 1.0) < 1e-10
        else:
            # If x and y are different, the similarity should be less than 1
            return similarity_value < 1.0 - 1e-10
    except Exception as e:
        logger.error(f"Error checking identity of discernibles: {str(e)}")
        raise