deepset-ai--haystack/docs-website/reference_versioned_docs/version-2.25/haystack-api/evaluation_api.md

title, id, description, slug

title	id	description	slug
Evaluation	evaluation-api	Represents the results of evaluation.	/evaluation-api

eval_run_result

EvaluationRunResult

Contains the inputs and the outputs of an evaluation pipeline and provides methods to inspect them.

init

__init__(
    run_name: str,
    inputs: dict[str, list[Any]],
    results: dict[str, dict[str, Any]],
)

Initialize a new evaluation run result.

Parameters:

• run_name (str) – Name of the evaluation run.
• inputs (dict[str, list[Any]]) – Dictionary containing the inputs used for the run. Each key is the name of the input and its value is a list of input values. The length of the lists should be the same.
• results (dict[str, dict[str, Any]]) – Dictionary containing the results of the evaluators used in the evaluation pipeline. Each key is the name of the metric and its value is dictionary with the following keys:
  • 'score': The aggregated score for the metric.
  • 'individual_scores': A list of scores for each input sample.

aggregated_report

aggregated_report(
    output_format: Literal["json", "csv", "df"] = "json",
    csv_file: str | None = None,
) -> Union[dict[str, list[Any]], DataFrame, str]

Generates a report with aggregated scores for each metric.

Parameters:

• output_format (Literal['json', 'csv', 'df']) – The output format for the report, "json", "csv", or "df", default to "json".
• csv_file (str | None) – Filepath to save CSV output if output_format is "csv", must be provided.

Returns:

• Union[dict[str, list[Any]], DataFrame, str] – JSON or DataFrame with aggregated scores, in case the output is set to a CSV file, a message confirming the successful write or an error message.

detailed_report

detailed_report(
    output_format: Literal["json", "csv", "df"] = "json",
    csv_file: str | None = None,
) -> Union[dict[str, list[Any]], DataFrame, str]

Generates a report with detailed scores for each metric.

Parameters:

• output_format (Literal['json', 'csv', 'df']) – The output format for the report, "json", "csv", or "df", default to "json".
• csv_file (str | None) – Filepath to save CSV output if output_format is "csv", must be provided.

Returns:

• Union[dict[str, list[Any]], DataFrame, str] – JSON or DataFrame with the detailed scores, in case the output is set to a CSV file, a message confirming the successful write or an error message.

comparative_detailed_report

comparative_detailed_report(
    other: EvaluationRunResult,
    keep_columns: list[str] | None = None,
    output_format: Literal["json", "csv", "df"] = "json",
    csv_file: str | None = None,
) -> Union[str, DataFrame, None]

Generates a report with detailed scores for each metric from two evaluation runs for comparison.

Parameters:

• other (EvaluationRunResult) – Results of another evaluation run to compare with.
• keep_columns (list[str] | None) – List of common column names to keep from the inputs of the evaluation runs to compare.
• output_format (Literal['json', 'csv', 'df']) – The output format for the report, "json", "csv", or "df", default to "json".
• csv_file (str | None) – Filepath to save CSV output if output_format is "csv", must be provided.

Returns:

• Union[str, DataFrame, None] – JSON or DataFrame with a comparison of the detailed scores, in case the output is set to a CSV file, a message confirming the successful write or an error message.

Raises:

• TypeError – If other is not an EvaluationRunResult instance, or if the detailed reports are not dictionaries.
• ValueError – If the other parameter is missing required attributes.