Class swarmauri_evaluator_externalimports.ExternalImportsEvaluator.ExternalImportsEvaluator

swarmauri_evaluator_externalimports.ExternalImportsEvaluator.ExternalImportsEvaluator

ExternalImportsEvaluator(**data)

Bases: EvaluatorBase

Evaluator that detects and penalizes non-built-in Python imports in source files.

This evaluator analyzes import statements to identify modules that are not part of the Python standard library. It helps to assess the external dependencies of a program.

ATTRIBUTE	DESCRIPTION
type	The literal type identifier for this evaluator. TYPE: Literal['ExternalImportsEvaluator']
standard_modules	A set of module names that are part of the Python standard library. TYPE: Set[str]

Initialize the ExternalImportsEvaluator.

PARAMETER	DESCRIPTION
**data	Additional initialization parameters. DEFAULT: {}

Source code in swarmauri_evaluator_externalimports/ExternalImportsEvaluator.py

def __init__(self, **data):
    """
    Initialize the ExternalImportsEvaluator.

    Args:
        **data: Additional initialization parameters.
    """
    super().__init__(**data)
    # Initialize the standard library modules set
    self._initialize_standard_modules()

type class-attribute instance-attribute

type = 'ExternalImportsEvaluator'

standard_modules class-attribute instance-attribute

standard_modules = set()

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'

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

aggregate_scores

aggregate_scores(scores, metadata_list)

Aggregate multiple evaluation scores and their metadata.

This method provides a default implementation for aggregating scores from multiple evaluations, typically by averaging them.

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 - Dict[str, Any]: Aggregated metadata

Source code in swarmauri_base/evaluators/EvaluatorBase.py

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

    This method provides a default implementation for aggregating scores from
    multiple evaluations, typically by averaging them.

    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
            - 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 metadata
    aggregated_metadata = {
        "individual_scores": scores,
        "aggregation_method": "average",
        "score_count": len(scores),
    }

    # If all metadata dictionaries have the same keys, aggregate those values too
    if metadata_list:
        common_keys = set.intersection(
            *[set(meta.keys()) for meta in metadata_list]
        )
        for key in common_keys:
            # Skip keys that are not numeric or are already handled
            if key == "execution_time":
                aggregated_metadata[key] = sum(meta[key] for meta in metadata_list)
            elif all(isinstance(meta[key], (int, float)) for meta in metadata_list):
                aggregated_metadata[key] = sum(
                    meta[key] for meta in metadata_list
                ) / len(metadata_list)

    return aggregated_score, aggregated_metadata