Class swarmauri_evaluator_abstractmethods.AbstractMethodsEvaluator.AbstractMethodsEvaluator

swarmauri_evaluator_abstractmethods.AbstractMethodsEvaluator.AbstractMethodsEvaluator

Bases: EvaluatorBase, ComponentBase

Evaluator that verifies all methods in abstract base classes are properly marked as abstract.

This evaluator parses Python source code to identify classes that inherit from ABC (Abstract Base Class) and verifies that all methods defined in these classes are decorated with @abstractmethod. It helps enforce proper interface design by ensuring that abstract classes properly declare their contract.

type class-attribute instance-attribute

type = 'AbstractMethodsEvaluator'

ignore_private class-attribute instance-attribute

ignore_private = Field(
    default=True,
    description="Whether to ignore private methods (starting with _)",
)

ignore_dunder class-attribute instance-attribute

ignore_dunder = Field(
    default=True,
    description="Whether to ignore dunder methods (starting and ending with __)",
)

abc_base_classes class-attribute instance-attribute

abc_base_classes = Field(
    default=["ABC", "abc.ABC"],
    description="List of base class names that indicate an abstract class",
)

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=EVALUATOR.value)

version class-attribute instance-attribute

version = '0.1.0'

aggregate_scores

aggregate_scores(scores, metadata_list)

Aggregates multiple evaluation scores and their metadata.

Combines scores from multiple evaluations and aggregates their metadata to provide a comprehensive overview of abstract method compliance.

PARAMETER	DESCRIPTION
scores	List of individual scores to aggregate TYPE: List[float]
metadata_list	List of metadata dictionaries corresponding to each score TYPE: List[Dict[str, Any]]

RETURNS	DESCRIPTION
Tuple[float, Dict[str, Any]]	A tuple containing: - float: The aggregated score (average) - Dict[str, Any]: Aggregated metadata

Source code in swarmauri_evaluator_abstractmethods/AbstractMethodsEvaluator.py

def aggregate_scores(
    self, scores: List[float], metadata_list: List[Dict[str, Any]]
) -> Tuple[float, Dict[str, Any]]:
    """
    Aggregates multiple evaluation scores and their metadata.

    Combines scores from multiple evaluations and aggregates their metadata
    to provide a comprehensive overview of abstract method compliance.

    Args:
        scores: List of individual scores to aggregate
        metadata_list: List of metadata dictionaries corresponding to each score

    Returns:
        A tuple containing:
            - float: The aggregated score (average)
            - Dict[str, Any]: Aggregated metadata
    """
    if not scores:
        return 0.0, {"error": "No scores to aggregate"}

    # Default aggregation is to average the scores
    aggregated_score = sum(scores) / len(scores)

    # Combine all issues from all evaluations
    all_issues = []
    total_abstract_classes = 0
    total_methods = 0
    total_abstract_methods = 0
    total_missing_decorators = 0

    for metadata in metadata_list:
        all_issues.extend(metadata.get("issues", []))
        total_abstract_classes += metadata.get("total_abstract_classes", 0)
        total_methods += metadata.get("total_methods", 0)
        total_abstract_methods += metadata.get("total_abstract_methods", 0)
        total_missing_decorators += metadata.get("total_missing_decorators", 0)

    # Create aggregated metadata
    aggregated_metadata = {
        "individual_scores": scores,
        "aggregation_method": "average",
        "score_count": len(scores),
        "issues": all_issues,
        "total_abstract_classes": total_abstract_classes,
        "total_methods": total_methods,
        "total_abstract_methods": total_abstract_methods,
        "total_missing_decorators": total_missing_decorators,
        "percentage_compliant": aggregated_score * 100,
    }

    return aggregated_score, aggregated_metadata

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

evaluate

evaluate(program, **kwargs)

Evaluate a program and return a fitness score with metadata.

This method wraps the concrete _compute_score implementation, capturing execution time and handling exceptions.

PARAMETER	DESCRIPTION
program	The program to evaluate TYPE: IProgram
**kwargs	Additional parameters for the evaluation process DEFAULT: {}

RETURNS	DESCRIPTION
Tuple[float, Dict[str, Any]]	A tuple containing: - float: A scalar fitness score (higher is better) - Dict[str, Any]: Metadata about the evaluation, including feature dimensions

RAISES	DESCRIPTION
EvaluationError	If the evaluation process fails
TypeError	If the program is not of the expected type

Source code in swarmauri_base/evaluators/EvaluatorBase.py

def evaluate(self, program: Program, **kwargs) -> Tuple[float, Dict[str, Any]]:
    """
    Evaluate a program and return a fitness score with metadata.

    This method wraps the concrete _compute_score implementation, capturing
    execution time and handling exceptions.

    Args:
        program: The program to evaluate
        **kwargs: Additional parameters for the evaluation process

    Returns:
        A tuple containing:
            - float: A scalar fitness score (higher is better)
            - Dict[str, Any]: Metadata about the evaluation, including feature dimensions

    Raises:
        EvaluationError: If the evaluation process fails
        TypeError: If the program is not of the expected type
    """
    if not isinstance(program, Program):
        raise TypeError(f"Expected Program type, got {type(program)}")

    start_time = time.time()

    try:
        # Call the concrete implementation to compute the score
        score, metadata = self._compute_score(program, **kwargs)

        # Add execution time to metadata
        execution_time = time.time() - start_time
        metadata["execution_time"] = execution_time

        logger.debug(
            f"Evaluation completed in {execution_time:.4f}s with score {score:.4f}"
        )
        return score, metadata

    except Exception as e:
        execution_time = time.time() - start_time
        logger.error(f"Evaluation failed after {execution_time:.4f}s: {str(e)}")
        raise EvaluationError(f"Evaluation failed: {str(e)}") from e

reset

reset()

Reset the evaluator to its initial state.

This method is called to clear any internal state or cached data before a new evaluation cycle begins.

Source code in swarmauri_base/evaluators/EvaluatorBase.py

def reset(self) -> None:
    """
    Reset the evaluator to its initial state.

    This method is called to clear any internal state or cached data
    before a new evaluation cycle begins.
    """
    # Reset logic can be implemented in subclasses if needed
    logger.debug("Resetting evaluator state")
    pass