Class swarmauri_base.evaluator_pools.EvaluatorPoolBase.EvaluatorPoolBase

swarmauri_base.evaluator_pools.EvaluatorPoolBase.EvaluatorPoolBase

EvaluatorPoolBase(**kwargs)

Bases: IEvaluatorPool, ComponentBase

Thread-safe evaluator-pool base class with registry, dispatch and aggregation.

Source code in swarmauri_base/evaluator_pools/EvaluatorPoolBase.py

def __init__(self, **kwargs: Any):
    super().__init__(**kwargs)
    self.evaluators = {}
    self.lock = threading.RLock()
    self.executor = None
    self.aggregation_func = (
        lambda scores: sum(scores) / len(scores) if scores else 0.0
    )

resource class-attribute instance-attribute

resource = EVALUATOR_POOL.value

model_config class-attribute instance-attribute

model_config = {'arbitrary_types_allowed': True}

evaluators class-attribute instance-attribute

evaluators = {}

lock class-attribute instance-attribute

lock = RLock()

executor class-attribute instance-attribute

executor = None

aggregation_func class-attribute instance-attribute

aggregation_func = (
    lambda scores: sum(scores) / len(scores)
    if scores
    else 0.0
)

type class-attribute instance-attribute

type = 'ComponentBase'

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

version class-attribute instance-attribute

version = '0.1.0'

initialize

initialize()

Source code in swarmauri_base/evaluator_pools/EvaluatorPoolBase.py

def initialize(self) -> None:
    try:
        self.executor = futures.ThreadPoolExecutor(max_workers=10)
        logger.info("EvaluatorPool initialised with thread-pool executor")
    except Exception as e:
        logger.exception("Initialisation failed")
        raise RuntimeError(f"Failed to initialise evaluator pool: {e}") from e

shutdown

shutdown()

Source code in swarmauri_base/evaluator_pools/EvaluatorPoolBase.py

def shutdown(self) -> None:
    try:
        if self.executor:
            self.executor.shutdown(wait=True)
            self.executor = None
        with self.lock:
            self.evaluators.clear()
        logger.info("EvaluatorPool shut down")
    except Exception as e:
        logger.exception("Shutdown failed")
        raise RuntimeError(f"Failed to shut down evaluator pool: {e}") from e

add_evaluator

add_evaluator(evaluator, name=None)

Source code in swarmauri_base/evaluator_pools/EvaluatorPoolBase.py

def add_evaluator(self, evaluator: IEvaluate, name: Optional[str] = None) -> str:
    if not isinstance(evaluator, IEvaluate):
        raise TypeError("Evaluator must implement IEvaluate")

    name = name or f"evaluator_{len(self.evaluators) + 1}"
    with self.lock:
        if name in self.evaluators:
            raise ValueError(f"Evaluator '{name}' already exists")
        self.evaluators[name] = evaluator
    logger.debug("Added evaluator '%s'", name)
    return name

remove_evaluator

remove_evaluator(name)

Source code in swarmauri_base/evaluator_pools/EvaluatorPoolBase.py

def remove_evaluator(self, name: str) -> bool:
    with self.lock:
        removed = self.evaluators.pop(name, None) is not None
    if removed:
        logger.debug("Removed evaluator '%s'", name)
    return removed

get_evaluator

get_evaluator(name)

Source code in swarmauri_base/evaluator_pools/EvaluatorPoolBase.py

def get_evaluator(self, name: str) -> Optional[IEvaluate]:
    with self.lock:
        return self.evaluators.get(name)

get_evaluator_names

get_evaluator_names()

Source code in swarmauri_base/evaluator_pools/EvaluatorPoolBase.py

def get_evaluator_names(self) -> List[str]:
    with self.lock:
        return list(self.evaluators.keys())

get_evaluator_count

get_evaluator_count()

Source code in swarmauri_base/evaluator_pools/EvaluatorPoolBase.py

def get_evaluator_count(self) -> int:
    with self.lock:
        return len(self.evaluators)

evaluate

evaluate(programs, **kwargs)

Source code in swarmauri_base/evaluator_pools/EvaluatorPoolBase.py

def evaluate(self, programs: Sequence[P], **kwargs) -> Sequence[IEvalResult]:
    try:
        processed = self.pre_process(programs)
        results = self._dispatch(processed)
        return self.post_process(results)
    except Exception as e:
        logger.exception("evaluate() failed")
        raise RuntimeError(f"Failed to evaluate programs: {e}") from e

evaluate_async async

evaluate_async(programs, **kwargs)

Source code in swarmauri_base/evaluator_pools/EvaluatorPoolBase.py

async def evaluate_async(
    self, programs: Sequence[P], **kwargs
) -> Sequence[IEvalResult]:
    loop = asyncio.get_event_loop()
    return await loop.run_in_executor(
        self.executor, partial(self.evaluate, programs, **kwargs)
    )

aggregate

aggregate(scores)

Source code in swarmauri_base/evaluator_pools/EvaluatorPoolBase.py

def aggregate(self, scores: Sequence[float]) -> float:
    if not scores:
        raise ValueError("Cannot aggregate an empty score list")
    return self.aggregation_func(list(scores))

set_aggregation_function

set_aggregation_function(func)

Source code in swarmauri_base/evaluator_pools/EvaluatorPoolBase.py

def set_aggregation_function(
    self, func: Callable[[Sequence[float]], float]
) -> None:
    if not callable(func):
        raise TypeError("Aggregation function must be callable")
    # sanity-check signature
    result = func([0.5, 0.5])
    if not isinstance(result, (int, float)):
        raise TypeError("Aggregation function must return a numeric value")
    self.aggregation_func = func

pre_process

pre_process(programs)

Source code in swarmauri_base/evaluator_pools/EvaluatorPoolBase.py

def pre_process(self, programs: Sequence[P]) -> Sequence[P]:
    return programs

post_process

post_process(results)

Source code in swarmauri_base/evaluator_pools/EvaluatorPoolBase.py

def post_process(self, results: Sequence[IEvalResult]) -> Sequence[IEvalResult]:
    return results

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