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BUG: Overlapping scorer-evaluation globs duplicate CSVs and change accuracy

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@its-amann 已经在做这个了。

开始于 2026年10月21日。

评估

难度
2/5
预计耗时
1-3 小时
新手友好度
78/100
Issue 类型
缺陷
描述清晰度
描述清楚
活跃度
活跃
技术栈
pandas, python
领域
backend, data

调研方向

从 pyrit/score/scorer_evaluation/scorer_evaluator.py 中的 run_evaluation_async() 开始，追踪 human_labeled_datasets_files 模式如何选择 CSV。为重叠和重复模式添加回归测试，同时保留版本字符串相同的不同文件；验证两种配置都会评估两个响应，准确率为 0.5。

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描述

Describe the bug

Overlapping human_labeled_datasets_files patterns load the same CSV more than once in run_evaluation_async(), changing sample weights and reported accuracy. For the same two files and deterministic scorer, adding an already-matched filename changes accuracy from 0.5 to 0.6667.

Steps/Code to Reproduce

No model API or external dataset needed. Only the dataset root is redirected; loading, scoring, and metrics use the real implementations.

import asyncio
from pathlib import Path
from tempfile import TemporaryDirectory
from unittest.mock import patch

from pyrit.memory import CentralMemory, SQLiteMemory
from pyrit.score import RegistryUpdateBehavior, SubStringScorer
from pyrit.score.scorer_evaluation import scorer_evaluator as se


async def run_async(root: Path) -> None:
    try:
        with patch.object(se, "SCORER_EVALS_PATH", root):
            for patterns in (["*.csv"], ["*.csv", "a.csv"]):
                evaluator = se.ObjectiveScorerEvaluator(SubStringScorer(substring="marker"))
                result = await evaluator.run_evaluation_async(
                    dataset_files=se.ScorerEvalDatasetFiles(
                        human_labeled_datasets_files=patterns, result_file="unused.jsonl"
                    ),
                    num_scorer_trials=1,
                    update_registry_behavior=RegistryUpdateBehavior.NEVER_UPDATE,
                )
                print(patterns, result.num_responses, round(float(result.accuracy), 4))
    finally:
        await CentralMemory.get_memory_instance().dispose_engine_async()


with TemporaryDirectory() as directory:
    root = Path(directory)
    for name, label in [("a.csv", 1), ("b.csv", 0)]:
        (root / name).write_text(
            "# dataset_version=1.0\nassistant_response,objective,human_score\n"
            f"marker,Synthetic objective,{label}\n",
            encoding="utf-8",
        )
    CentralMemory.set_memory_instance(SQLiteMemory(db_path=":memory:", silent=True))
    asyncio.run(run_async(root))
Expected Results

Both configurations evaluate the same two responses, with accuracy 0.5.

Actual Results
['*.csv'] 2 0.5
['*.csv', 'a.csv'] 3 0.6667

Would deduplicating matched paths before loading be appropriate? I'd keep distinct files with equal version strings intact and add regression tests for overlapping and repeated patterns. I'd like to take this if you mark it ready for contribution; I'll wait for triage before preparing a PR.

Versions

macOS ARM64, Python 3.12.13, PyRIT 1.2.0.dev0 (ac196e57, main), numpy 2.4.6, pandas 3.0.6, scipy 1.17.1. Dependencies installed with uv sync --locked --no-dev.

AI-assisted investigation; the reproduction above was executed locally against unmodified source.

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