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Abstract base class for creating custom evaluators in Fiddler Evals. The Evaluator class provides a flexible framework for creating builtin and custom evaluators that can assess LLM outputs against various criteria. Each evaluator is responsible for a single, specific evaluation task (e.g., hallucination detection, answer relevance, exact match, etc.). Parameter Mapping: : Evaluators can define their own parameter mappings using score_fn_kwargs_mapping in the constructor. These mappings specify how data from the evaluation context (inputs, outputs, expected_outputs) should be passed to the evaluator’s score method. Mapping Priority (highest to lowest):
  1. Evaluator-level score_fn_kwargs_mapping (set in constructor)
  2. Evaluation-level kwargs_mapping (passed to evaluate function)
  3. Default parameter resolution<br>
This allows evaluators to define sensible defaults while still permitting customization at the evaluation level. Creating Custom Evaluators: To create a custom evaluator, inherit from this class and implement the score method with parameters specific to your evaluation needs. Example - custom evaluator with parameter mapping: 
class ExactMatchEvaluator(Evaluator):
    def __init__(self, output_key: str = "answer", score_name_prefix: str = None):
        super().__init__(
            score_name_prefix=score_name_prefix,
            score_fn_kwargs_mapping={"output": output_key},
        )

    def score(self, output: str, expected_output: str) -> Score:
        is_match = output.strip().lower() == expected_output.strip().lower()
        return Score(
            name=f"{self.score_name_prefix}exact_match",
            value=1.0 if is_match else 0.0,
            reasoning=f"Match: {is_match}",
        )

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Parameters

ParameterTypeRequiredDefaultDescription
score_name_prefix`strNone`✗None
score_fn_kwargs_mapping`ScoreFnKwargsMappingTypeNone`✗None
The score method signature is intentionally flexible using *args and **kwargs to allow each evaluator to define its own parameter requirements. This design enables maximum flexibility while maintaining a consistent interface across all evaluators in the framework.
Initialize the evaluator with parameter mapping configuration.

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Parameters

ParameterTypeRequiredDefaultDescription
score_name_prefix`strNone`✗None
score_fn_kwargs_mapping`Dict[str, strCallable[[Dict[str, Any]], Any]] | None`✗None

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Example

# Simple string mapping
evaluator = MyEvaluator(score_fn_kwargs_mapping={"output": "answer"})

# Complex transformation function
evaluator = MyEvaluator(score_fn_kwargs_mapping={
        "question": lambda x: x["inputs"]["question"],
        "response": "answer"
    })

    # Using score name prefix for multiple instances
    evaluator1 = RegexSearch(r"\d+", score_name_prefix="question")
    evaluator2 = RegexSearch(r"\d+", score_name_prefix="answer")
    # Results in scores named "question_has_number" and "answer_has_number"

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Raises

ScoreFunctionInvalidArgs — If the mapping contains invalid parameter names that don’t match the evaluator’s score method signature. Return type: None

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property name : str

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abstractmethod score(*args, **kwargs)

Evaluate inputs and return a score or list of scores. This method must be implemented by all concrete evaluator classes. Each evaluator can define its own parameter signature based on what it needs for evaluation. Common parameter patterns:
  • Output-only: score(self, output: str) -> Score
  • Input-Output: score(self, input: str, output: str) -> Score
  • Comparison: score(self, output: str, expected_output: str) -> Score
  • All parameters: score(self, input: str, output: str, context: list[str]) -> Score

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Parameters

ParameterTypeRequiredDefaultDescription
*argsAny✗NonePositional arguments specific to the evaluator’s needs.

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Returns

A single Score object or list of Score objects : representing the evaluation results. Each Score should include:
  • name: The score name (e.g., “has_zipcode”)
  • evaluator_name: The evaluator name (e.g., “RegexMatch”)
  • value: The score value (typically 0.0 to 1.0)
  • status: SUCCESS, FAILED, or SKIPPED
  • reasoning: Optional explanation of the score
  • error: Optional error information if evaluation failed
Return type: Score | list[Score]

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Raises

  • ValueError — If required parameters are missing or invalid.
  • TypeError — If parameters have incorrect types.
  • Exception — Any other evaluation-specific errors.