Class swarmauri_standard.similarities.TanimotoSimilarity.TanimotoSimilarity

swarmauri_standard.similarities.TanimotoSimilarity.TanimotoSimilarity

Bases: SimilarityBase

Tanimoto Similarity implementation, a generalization of Jaccard for real vectors.

The Tanimoto coefficient is widely used in cheminformatics for measuring the similarity between molecular fingerprints. It is defined as the ratio of the intersection to the union when applied to binary vectors, and extends to real-valued vectors.

For real-valued vectors, the formula is: T(A,B) = (A·B) / (|A|^2 + |B|^2 - A·B)

where A·B is the dot product, and |A|^2 is the sum of squares of elements.

Attributes

type : Literal["TanimotoSimilarity"] Type identifier for this similarity measure

type class-attribute instance-attribute

type = 'TanimotoSimilarity'

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 Tanimoto similarity between two vectors.

Parameters

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

Returns

float Tanimoto similarity score between x and y, in range [0,1]

Raises

ValueError If vectors have different dimensions or are zero vectors TypeError If inputs cannot be converted to numeric arrays

Source code in swarmauri_standard/similarities/TanimotoSimilarity.py

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

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

    Returns
    -------
    float
        Tanimoto similarity score between x and y, in range [0,1]

    Raises
    ------
    ValueError
        If vectors have different dimensions or are zero vectors
    TypeError
        If inputs cannot be converted to numeric arrays
    """
    try:
        x_array, y_array = self._validate_input(x, y)

        # Check if vectors are proportional (one is a scalar multiple of the other)
        # Find ratio for first non-zero element pair
        ratio = None
        for i in range(len(x_array)):
            if y_array[i] != 0 and x_array[i] != 0:
                ratio = x_array[i] / y_array[i]
                break

        if ratio is not None:
            # Check if all elements maintain this ratio (allowing for floating point error)
            is_proportional = True
            for i in range(len(x_array)):
                if x_array[i] == 0 and y_array[i] == 0:
                    continue  # Both elements are zero, ratio is preserved
                elif x_array[i] == 0 or y_array[i] == 0:
                    is_proportional = False  # One element is zero, the other isn't
                    break
                elif abs(x_array[i] / y_array[i] - ratio) > 1e-10:
                    is_proportional = False  # Ratio differs
                    break

            if is_proportional:
                return 1.0  # Proportional vectors have similarity 1.0

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

        # Calculate sum of squares
        sum_squares_x = np.sum(x_array**2)
        sum_squares_y = np.sum(y_array**2)

        # Calculate Tanimoto coefficient
        denominator = sum_squares_x + sum_squares_y - dot_product

        # Avoid division by zero (though we already checked for zero vectors)
        if denominator == 0:
            return 0.0

        tanimoto = dot_product / denominator

        logger.debug(f"Tanimoto similarity: {tanimoto}")
        return float(tanimoto)
    except Exception as e:
        logger.error(f"Error calculating Tanimoto similarity: {str(e)}")
        raise

similarities

similarities(x, ys)

Calculate Tanimoto similarities between one vector and multiple other vectors.

This implementation is optimized for multiple comparisons.

Parameters

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

Returns

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

Raises

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

Source code in swarmauri_standard/similarities/TanimotoSimilarity.py

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

    This implementation is optimized for multiple comparisons.

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

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

    Raises
    ------
    ValueError
        If any vectors have different dimensions or are zero vectors
    TypeError
        If any inputs cannot be converted to numeric arrays
    """
    try:
        # Convert reference vector to numpy array
        x_array = np.array(x, dtype=float)

        # Check for zero vector
        if np.all(x_array == 0):
            raise ValueError("Tanimoto similarity is not defined for zero vectors")

        # Precompute sum of squares for reference vector
        sum_squares_x = np.sum(x_array**2)

        results = []
        for y in ys:
            try:
                y_array = np.array(y, dtype=float)

                # Check dimensions
                if x_array.shape != y_array.shape:
                    raise ValueError(
                        f"Input vectors must have the same dimensions: {x_array.shape} != {y_array.shape}"
                    )

                # Check for zero vector
                if np.all(y_array == 0):
                    raise ValueError(
                        "Tanimoto similarity is not defined for zero vectors"
                    )

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

                # Calculate sum of squares for y
                sum_squares_y = np.sum(y_array**2)

                # Calculate Tanimoto coefficient
                denominator = sum_squares_x + sum_squares_y - dot_product

                # Avoid division by zero
                if denominator == 0:
                    results.append(0.0)
                else:
                    results.append(float(dot_product / denominator))

            except Exception as e:
                logger.warning(f"Error calculating individual similarity: {str(e)}")
                results.append(
                    float("nan")
                )  # Use NaN to indicate calculation error

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

dissimilarity

dissimilarity(x, y)

Calculate the Tanimoto dissimilarity between two vectors.

Parameters

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

Returns

float Tanimoto dissimilarity score between x and y, in range [0,1]

Raises

ValueError If vectors have different dimensions or are zero vectors TypeError If inputs cannot be converted to numeric arrays

Source code in swarmauri_standard/similarities/TanimotoSimilarity.py

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

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

    Returns
    -------
    float
        Tanimoto dissimilarity score between x and y, in range [0,1]

    Raises
    ------
    ValueError
        If vectors have different dimensions or are zero vectors
    TypeError
        If inputs cannot be converted to numeric arrays
    """
    try:
        # Tanimoto dissimilarity is 1 - similarity
        return 1.0 - self.similarity(x, y)
    except Exception as e:
        logger.error(f"Error calculating Tanimoto dissimilarity: {str(e)}")
        raise

check_bounded

check_bounded()

Check if the Tanimoto similarity measure is bounded.

Tanimoto similarity is bounded in the range [0,1].

Returns

bool True, as Tanimoto similarity is bounded in [0,1]

Source code in swarmauri_standard/similarities/TanimotoSimilarity.py

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

    Tanimoto similarity is bounded in the range [0,1].

    Returns
    -------
    bool
        True, as Tanimoto similarity is bounded in [0,1]
    """
    return True

check_reflexivity

check_reflexivity(x)

Check if the Tanimoto 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

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

Source code in swarmauri_standard/similarities/TanimotoSimilarity.py

def check_reflexivity(self, x: ComparableType) -> bool:
    """
    Check if the Tanimoto 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
    ------
    TypeError
        If the input cannot be converted to a numeric array
    ValueError
        If the input is a zero vector
    """
    try:
        # Convert to numpy array
        x_array = np.array(x, dtype=float)

        # Check for zero vector
        if np.all(x_array == 0):
            raise ValueError("Tanimoto similarity is not defined for zero vectors")

        # For non-zero vectors, Tanimoto similarity is reflexive
        # s(x,x) = (x·x) / (|x|^2 + |x|^2 - x·x) = |x|^2 / |x|^2 = 1

        # Calculate explicitly to verify
        dot_product = np.sum(x_array**2)  # x·x = sum of squares
        denominator = (
            dot_product + dot_product - dot_product
        )  # 2|x|^2 - |x|^2 = |x|^2

        similarity_value = dot_product / denominator

        # Use approximate equality to handle floating-point precision issues
        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 Tanimoto 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 vectors have different dimensions or are zero vectors TypeError If inputs cannot be converted to numeric arrays

Source code in swarmauri_standard/similarities/TanimotoSimilarity.py

def check_symmetry(self, x: ComparableType, y: ComparableType) -> bool:
    """
    Check if the Tanimoto 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 vectors have different dimensions or are zero vectors
    TypeError
        If inputs cannot be converted to numeric arrays
    """
    try:
        # Tanimoto similarity is symmetric by definition
        # s(x,y) = (x·y) / (|x|^2 + |y|^2 - x·y) = (y·x) / (|y|^2 + |x|^2 - y·x) = s(y,x)

        # Calculate explicitly to verify
        similarity_xy = self.similarity(x, y)
        similarity_yx = self.similarity(y, x)

        # Use approximate equality to handle floating-point precision issues
        return abs(similarity_xy - similarity_yx) < 1e-10
    except Exception as e:
        logger.error(f"Error checking symmetry: {str(e)}")
        raise

check_identity_of_discernibles

check_identity_of_discernibles(x, y)

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

For Tanimoto similarity, s(x,y) = 1 if and only if x and y are proportional vectors.

Parameters

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

Returns

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

Raises

ValueError If vectors have different dimensions or are zero vectors TypeError If inputs cannot be converted to numeric arrays

Source code in swarmauri_standard/similarities/TanimotoSimilarity.py

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

    For Tanimoto similarity, s(x,y) = 1 if and only if x and y are proportional vectors.

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

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

    Raises
    ------
    ValueError
        If vectors have different dimensions or are zero vectors
    TypeError
        If inputs cannot be converted to numeric arrays
    """
    try:
        x_array, y_array = self._validate_input(x, y)

        # Calculate similarity
        similarity_value = self.similarity(x_array, y_array)

        # For Tanimoto, s(x,y) = 1 if and only if x and y are proportional vectors
        # Check if vectors are proportional (x = c*y for some constant c)
        if abs(similarity_value - 1.0) < 1e-10:
            # If similarity is 1, vectors should be proportional
            # Find ratio for first non-zero element
            ratio = None
            for i in range(len(x_array)):
                if y_array[i] != 0 and x_array[i] != 0:
                    ratio = x_array[i] / y_array[i]
                    break

            if ratio is None:
                # This shouldn't happen since we checked for zero vectors
                return False

            # Check if all elements maintain this ratio (allowing for floating point error)
            for i in range(len(x_array)):
                if x_array[i] == 0 and y_array[i] == 0:
                    continue  # Both elements are zero, ratio is preserved
                elif x_array[i] == 0 or y_array[i] == 0:
                    return False  # One element is zero, the other isn't
                elif abs(x_array[i] / y_array[i] - ratio) > 1e-10:
                    return False  # Ratio differs

            return True  # All elements maintain the same ratio
        else:
            # If similarity is not 1, vectors should not be proportional
            # Check if vectors are not proportional
            # We already know similarity < 1, so they're not proportional
            return True
    except Exception as e:
        logger.error(f"Error checking identity of discernibles: {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