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Implement a Judge Calibration Pipeline

You have an LLM-as-judge that scores RAG responses for faithfulness, but you don't know if it agrees with humans. Build a calibration pipeline that measures how well the judge aligns with human labels and flags when calibration degrades.

Requirements

1. JudgeCalibrationRun dataclass: stores case_id, judge_score, human_score, dimension (faithfulness/relevance/helpfulness), and timestamp.

2. compute_calibration_metrics(runs: list[JudgeCalibrationRun]) -> dict that returns:

   • agreement_rate: fraction of cases where abs(judge_score - human_score) <= 0.2
   • mean_absolute_error: average abs(judge_score - human_score)
   • bias: mean of judge_score - human_score (positive = judge rates higher than humans)
   • cohens_kappa: Cohen's Kappa treating scores >= 0.7 as "pass" and < 0.7 as "fail"
   • by_dimension: the above metrics broken down per dimension

3. is_calibration_healthy(metrics: dict) -> tuple[bool, list[str]] that returns (True, []) when calibration is acceptable, or (False, [reason, ...]) when it fails. Flag when: agreement_rate < 0.75, mean_absolute_error > 0.25, or abs(bias) > 0.15.

4. A standalone cohens_kappa(predicted: list[bool], actual: list[bool]) -> float function using the formula (p_o - p_e) / (1 - p_e) where p_o is observed agreement and p_e is expected agreement by chance.

Constraints

• No external ML libraries. Pure Python only.
• compute_calibration_metrics must handle the case where a dimension has no data (skip it in by_dimension).
• All scores are floats in [0.0, 1.0].