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Class swarmauri_standard.similarities.JaccardIndexSimilarity.JaccardIndexSimilarity

swarmauri_standard.similarities.JaccardIndexSimilarity.JaccardIndexSimilarity

Bases: SimilarityBase

Jaccard Index similarity measure for sets.

The Jaccard Index is defined as the size of the intersection divided by the size of the union of the sample sets. It ranges from 0 (no similarity) to 1 (identical sets).

This similarity measure is symmetric and bounded in the range [0,1].

Attributes

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

type class-attribute instance-attribute 

type = 'JaccardIndexSimilarity'

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 Jaccard Index similarity between two sets.

Parameters

x : Set First set to compare y : Set Second set to compare

Returns

float Jaccard Index similarity score between x and y

Raises

TypeError If inputs are not sets ValueError If either set is empty and the other is not

Source code in swarmauri_standard/similarities/JaccardIndexSimilarity.py 
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def similarity(self, x: Set, y: Set) -> float:
    """
    Calculate the Jaccard Index similarity between two sets.

    Parameters
    ----------
    x : Set
        First set to compare
    y : Set
        Second set to compare

    Returns
    -------
    float
        Jaccard Index similarity score between x and y

    Raises
    ------
    TypeError
        If inputs are not sets
    ValueError
        If either set is empty and the other is not
    """
    # Validate input types
    if not isinstance(x, set) or not isinstance(y, set):
        logger.error("JaccardIndexSimilarity requires set inputs")
        raise TypeError("Inputs must be sets")

    # Handle edge cases
    if len(x) == 0 and len(y) == 0:
        logger.debug("Both sets are empty, returning similarity of 1.0")
        return 1.0

    if len(x) == 0 or len(y) == 0:
        logger.debug("One set is empty, returning similarity of 0.0")
        return 0.0

    # Calculate intersection and union
    intersection_size = len(x.intersection(y))
    union_size = len(x.union(y))

    # Calculate Jaccard Index
    jaccard_index = intersection_size / union_size
    logger.debug(f"Calculated Jaccard Index: {jaccard_index}")

    return jaccard_index

similarities 

similarities(x, ys)

Calculate Jaccard Index similarities between one set and multiple other sets.

Parameters

x : Set Reference set ys : Sequence[Set] Sequence of sets to compare against the reference

Returns

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

Raises

TypeError If any input is not a set

Source code in swarmauri_standard/similarities/JaccardIndexSimilarity.py 
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def similarities(self, x: Set, ys: Sequence[Set]) -> List[float]:
    """
    Calculate Jaccard Index similarities between one set and multiple other sets.

    Parameters
    ----------
    x : Set
        Reference set
    ys : Sequence[Set]
        Sequence of sets to compare against the reference

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

    Raises
    ------
    TypeError
        If any input is not a set
    """
    # Validate input types
    if not isinstance(x, set):
        logger.error("Reference input must be a set")
        raise TypeError("Reference input must be a set")

    results = []

    for i, y in enumerate(ys):
        if not isinstance(y, set):
            logger.error(f"Input at index {i} is not a set")
            raise TypeError("All inputs in sequence must be sets")

        results.append(self.similarity(x, y))

    return results

dissimilarity 

dissimilarity(x, y)

Calculate the Jaccard dissimilarity between two sets.

Jaccard dissimilarity is defined as 1 - Jaccard similarity.

Parameters

x : Set First set to compare y : Set Second set to compare

Returns

float Jaccard dissimilarity score between x and y

Raises

TypeError If inputs are not sets

Source code in swarmauri_standard/similarities/JaccardIndexSimilarity.py 
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def dissimilarity(self, x: Set, y: Set) -> float:
    """
    Calculate the Jaccard dissimilarity between two sets.

    Jaccard dissimilarity is defined as 1 - Jaccard similarity.

    Parameters
    ----------
    x : Set
        First set to compare
    y : Set
        Second set to compare

    Returns
    -------
    float
        Jaccard dissimilarity score between x and y

    Raises
    ------
    TypeError
        If inputs are not sets
    """
    # Since Jaccard Index is bounded between 0 and 1,
    # we can define dissimilarity as 1 - similarity
    return 1.0 - self.similarity(x, y)

check_bounded 

check_bounded()

Check if the similarity measure is bounded.

The Jaccard Index is always bounded in the range [0,1].

Returns

bool True, as the Jaccard Index is bounded

Source code in swarmauri_standard/similarities/JaccardIndexSimilarity.py 
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def check_bounded(self) -> bool:
    """
    Check if the similarity measure is bounded.

    The Jaccard Index is always bounded in the range [0,1].

    Returns
    -------
    bool
        True, as the Jaccard Index is bounded
    """
    return True

check_symmetry 

check_symmetry(x, y)

Check if the similarity measure is symmetric.

The Jaccard Index is symmetric by definition: J(A,B) = J(B,A).

Parameters

x : Set First set to compare y : Set Second set to compare

Returns

bool True, as the Jaccard Index is symmetric

Raises

TypeError If inputs are not sets

Source code in swarmauri_standard/similarities/JaccardIndexSimilarity.py 
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def check_symmetry(self, x: Set, y: Set) -> bool:
    """
    Check if the similarity measure is symmetric.

    The Jaccard Index is symmetric by definition: J(A,B) = J(B,A).

    Parameters
    ----------
    x : Set
        First set to compare
    y : Set
        Second set to compare

    Returns
    -------
    bool
        True, as the Jaccard Index is symmetric

    Raises
    ------
    TypeError
        If inputs are not sets
    """
    # Validate input types
    if not isinstance(x, set) or not isinstance(y, set):
        logger.error("Inputs must be sets")
        raise TypeError("Inputs must be sets")

    # The Jaccard Index is symmetric by definition, but we can verify
    similarity_xy = self.similarity(x, y)
    similarity_yx = self.similarity(y, x)

    return abs(similarity_xy - similarity_yx) < 1e-10

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 
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@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 
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@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 
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@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 
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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 
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@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 
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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 
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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 
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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_reflexivity 

check_reflexivity(x)

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

Parameters

x : ComparableType Object to check reflexivity with

Returns

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

Raises

TypeError If the input type is not supported

Source code in swarmauri_base/similarities/SimilarityBase.py 
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def check_reflexivity(self, x: ComparableType) -> bool:
    """
    Check if the similarity measure is reflexive: s(x,x) = 1.

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

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

    Raises
    ------
    TypeError
        If the input type is not supported
    """
    try:
        # A similarity measure is reflexive if s(x,x) = 1
        similarity_value = self.similarity(x, x)
        # 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_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 
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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