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fix lint
This commit is contained in:
MaiziXiao
parent
7938f352d7
commit
7f31ef7357
@ -57,7 +57,7 @@
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## 🚀 最新进展 <a><img width="35" height="20" src="https://user-images.githubusercontent.com/12782558/212848161-5e783dd6-11e8-4fe0-bbba-39ffb77730be.png"></a>
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- **\[2025.03.11\]** 现已支持 `SuperGPQA` LLM知识能力评测，欢迎尝试🔥🔥🔥
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- **\[2025.03.11\]** 现已支持 `SuperGPQA` LLM知识能力评测，欢迎尝试！🔥🔥🔥
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- **\[2025.02.28\]** 我们为 `DeepSeek-R1` 系列模型添加了教程，请查看 [评估推理模型](docs/en/user_guides/deepseek_r1.md) 了解更多详情！🔥🔥🔥
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- **\[2025.02.15\]** 我们新增了两个实用的评测工具：用于LLM作为评判器的`GenericLLMEvaluator`和用于数学推理评估的`MATHEvaluator`。查看[LLM评判器](docs/zh_cn/advanced_guides/llm_judge.md)和[数学能力评测](docs/zh_cn/advanced_guides/general_math.md)文档了解更多详情！🔥🔥🔥
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- **\[2025.01.16\]** 我们现已支持 [InternLM3-8B-Instruct](https://huggingface.co/internlm/internlm3-8b-instruct) 模型，该模型在推理、知识类任务上取得同量级最优性能，欢迎尝试。
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@ -11,13 +11,13 @@ from opencompass.openicl.icl_retriever import ZeroRetriever
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reader_cfg = dict(
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input_columns=[
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'question',
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"options",
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'options',
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'discipline',
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'field',
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'subfield',
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'difficulty',
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"infer_prompt",
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"prompt_mode",
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'infer_prompt',
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'prompt_mode',
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],
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output_column='answer_letter',
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)
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@ -47,7 +47,7 @@ eval_cfg = dict(
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supergpqa_dataset = dict(
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type=SuperGPQADataset,
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abbr='supergpqa',
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path="m-a-p/SuperGPQA",
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path='m-a-p/SuperGPQA',
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prompt_mode='zero-shot',
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reader_cfg=reader_cfg,
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infer_cfg=infer_cfg,
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@ -127,6 +127,7 @@ from .strategyqa import * # noqa: F401, F403
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from .subjective import * # noqa: F401, F403
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from .summedits import * # noqa: F401, F403
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from .summscreen import * # noqa: F401, F403
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from .supergpqa import * # noqa: F401, F403
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from .svamp import * # noqa: F401, F403
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from .tabmwp import * # noqa: F401, F403
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from .taco import * # noqa: F401, F403
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@ -147,4 +148,3 @@ from .xcopa import * # noqa: F401, F403
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from .xiezhi import XiezhiDataset, XiezhiRetriever # noqa: F401, F403
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from .xlsum import * # noqa: F401, F403
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from .xsum import * # noqa: F401, F403
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from .supergpqa import *
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@ -1,38 +1,23 @@
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import csv
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import json
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import os.path as osp
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from os import environ
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from datasets import load_dataset
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import os
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from datasets import Dataset, DatasetDict
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from opencompass.datasets.supergpqa.supergpqa_utils import (
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evaluate_responses,
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find_file,
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load_json_or_jsonl,
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load_json_or_jsonl_with_idx,
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load_yaml,
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)
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from datasets import Dataset, load_dataset
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from opencompass.datasets.supergpqa.supergpqa_eval import (
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extract_option_content, extract_option_labels)
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from opencompass.datasets.supergpqa.supergpqa_utils import load_yaml
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from opencompass.openicl.icl_evaluator import BaseEvaluator
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from opencompass.registry import ICL_EVALUATORS, LOAD_DATASET
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import unittest
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from opencompass.utils import get_data_path
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from opencompass.datasets.supergpqa.supergpqa_eval import (
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extract_option_labels,
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extract_option_content,
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)
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from ..base import BaseDataset
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def _parse(item, template, prompt_mode):
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prompt_format = [
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item['question']
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+ '\n'
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+ '\n'.join(
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[
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f'{chr(65+i)}) {option}'
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for i, option in enumerate(item['options'])
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]
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)
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item['question'] + '\n' + '\n'.join([
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f'{chr(65+i)}) {option}'
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for i, option in enumerate(item['options'])
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])
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]
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item['infer_prompt'] = template['prompt_format'][0].format(*prompt_format)
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item['prompt_mode'] = prompt_mode
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@ -41,6 +26,7 @@ def _parse(item, template, prompt_mode):
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@LOAD_DATASET.register_module()
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class SuperGPQADataset(BaseDataset):
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@staticmethod
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def load(path: str, prompt_mode: str, **kwargs):
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path = get_data_path(path, local_mode=True)
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@ -80,126 +66,119 @@ class SuperGPQAEvaluator(BaseEvaluator):
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count = 0
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err = 0
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miss = 0
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acc_difficulty = {"hard": 0, "middle": 0, "easy": 0}
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count_difficulty = {"hard": 0, "middle": 0, "easy": 0}
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acc_difficulty = {'hard': 0, 'middle': 0, 'easy': 0}
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count_difficulty = {'hard': 0, 'middle': 0, 'easy': 0}
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stats = {'discipline': {}, 'field': {}, 'subfield': {}}
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details = []
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for i, sample in enumerate(test_set):
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sample["pred"] = prediction = predictions[i]
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sample['pred'] = prediction = predictions[i]
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gold = references[i]
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if mode == 'zero-shot':
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predict = extract_option_labels(prediction, 'ABCDEFGHIJ')
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if predict == None:
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predict = extract_option_content(
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prediction, sample["options"]
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)
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predict = (
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chr(sample["options"].index(predict) + 65)
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if predict
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else None
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)
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sample["extracted_answer"] = predict
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if predict is None:
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predict = extract_option_content(prediction,
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sample['options'])
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predict = (chr(sample['options'].index(predict) +
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65) if predict else None)
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sample['extracted_answer'] = predict
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elif mode == 'five-shot':
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response = prediction.split('Question:')[0]
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predict = extract_option_labels(response, 'ABCDEFGHIJ')
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if predict == None:
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predict = extract_option_content(
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response, sample["options"]
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)
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predict = (
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chr(sample["options"].index(predict) + 65)
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if predict
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else None
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)
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if predict == None:
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if predict is None:
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predict = extract_option_content(response,
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sample['options'])
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predict = (chr(sample['options'].index(predict) +
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65) if predict else None)
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if predict is None:
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predict = extract_option_labels(prediction, 'ABCDEFGHIJ')
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if predict == None:
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if predict is None:
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predict = extract_option_content(
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prediction, sample["options"]
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)
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predict = (
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chr(sample["options"].index(predict) + 65)
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if predict
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else None
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)
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sample["extracted_answer"] = predict
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prediction, sample['options'])
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predict = (chr(sample['options'].index(predict) +
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65) if predict else None)
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sample['extracted_answer'] = predict
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discipline = sample.get("discipline", "unknown")
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field = sample.get("field", "unknown")
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subfield = sample.get("subfield", "unknown")
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difficulty = sample.get("difficulty", "unknown")
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discipline = sample.get('discipline', 'unknown')
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field = sample.get('field', 'unknown')
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subfield = sample.get('subfield', 'unknown')
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difficulty = sample.get('difficulty', 'unknown')
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for level, key in [
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('discipline', discipline),
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# ('field', f"{discipline}/{field}"),
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# ('subfield', f"{discipline}/{field}/{subfield}"),
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# ('field', f"{discipline}/{field}"),
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# ('subfield', f"{discipline}/{field}/{subfield}"),
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]:
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if key not in stats[level]:
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stats[level][key] = {
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"correct": 0,
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"total": 0,
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"miss": 0,
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"error": 0,
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"discipline": discipline,
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"field": field,
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"subfield": subfield,
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"difficulty": {
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"easy": {"correct": 0, "total": 0},
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"middle": {"correct": 0, "total": 0},
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"hard": {"correct": 0, "total": 0},
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'correct': 0,
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'total': 0,
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'miss': 0,
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'error': 0,
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'discipline': discipline,
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'field': field,
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'subfield': subfield,
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'difficulty': {
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'easy': {
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'correct': 0,
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'total': 0
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},
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'middle': {
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'correct': 0,
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'total': 0
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},
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'hard': {
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'correct': 0,
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'total': 0
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},
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},
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}
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stats[level][key]["total"] += 1
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stats[level][key]["difficulty"][difficulty]["total"] += 1
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stats[level][key]['total'] += 1
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stats[level][key]['difficulty'][difficulty]['total'] += 1
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answer_letter = sample["answer_letter"]
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answer_letter = sample['answer_letter']
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assert answer_letter == gold
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if predict and answer_letter == predict:
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acc += 1
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acc_difficulty[difficulty] += 1
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sample["status"] = "correct"
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stats[level][key]["correct"] += 1
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stats[level][key]["difficulty"][difficulty]["correct"] += 1
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elif predict == None or predict == "":
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sample['status'] = 'correct'
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stats[level][key]['correct'] += 1
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stats[level][key]['difficulty'][difficulty]['correct'] += 1
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elif predict == None or predict == '':
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miss += 1
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sample["status"] = "miss"
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stats[level][key]["miss"] += 1
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sample['status'] = 'miss'
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stats[level][key]['miss'] += 1
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elif predict == 'error':
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err += 1
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sample["status"] = "error"
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stats[level][key]["error"] += 1
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sample['status'] = 'error'
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stats[level][key]['error'] += 1
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else:
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sample["status"] = "incorrect"
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sample['status'] = 'incorrect'
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count += 1
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count_difficulty[difficulty] += 1
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details.append(
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{
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'pred': sample['pred'],
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'answer': sample['answer'],
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'parsed_answer': sample['extracted_answer'],
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'correct': True if sample['status'] else False,
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}
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)
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details.append({
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'pred': sample['pred'],
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'answer': sample['answer'],
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'parsed_answer': sample['extracted_answer'],
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'correct': True if sample['status'] else False,
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})
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return {
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'accuracy': acc / count if count > 0 else 0,
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'error_rate': err / count if count > 0 else 0,
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'miss_rate': miss / count if count > 0 else 0,
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'hard_accuracy': (
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acc_difficulty["hard"] / count_difficulty["hard"]
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if count_difficulty["hard"] > 0
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else 0
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),
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'middle_accuracy': (
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acc_difficulty["middle"] / count_difficulty["middle"]
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if count_difficulty["middle"] > 0
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else 0
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),
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'easy_accuracy': (
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acc_difficulty["easy"] / count_difficulty["easy"]
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if count_difficulty["easy"] > 0
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else 0
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),
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'details': details,
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'accuracy':
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acc / count if count > 0 else 0,
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'error_rate':
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err / count if count > 0 else 0,
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'miss_rate':
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miss / count if count > 0 else 0,
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'hard_accuracy':
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(acc_difficulty['hard'] /
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count_difficulty['hard'] if count_difficulty['hard'] > 0 else 0),
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'middle_accuracy':
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(acc_difficulty['middle'] / count_difficulty['middle']
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if count_difficulty['middle'] > 0 else 0),
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'easy_accuracy':
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(acc_difficulty['easy'] /
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count_difficulty['easy'] if count_difficulty['easy'] > 0 else 0),
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'details':
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details,
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}
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@ -1,7 +1,8 @@
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import yaml
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import uuid
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class ConfigWrapper:
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def __init__(self, config_path):
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self._config = {}
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with open(config_path, 'r') as file:
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@ -15,37 +16,73 @@ class ConfigWrapper:
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else:
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self._config[key] = value
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super().__setattr__(key, value)
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def __getattr__(self, key):
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if key in self._config:
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return self._config[key]
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raise AttributeError(f"'ConfigWrapper' object has no attribute '{key}'")
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raise AttributeError(
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f"'ConfigWrapper' object has no attribute '{key}'")
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def get_id(self, data):
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if isinstance(self._config.get('id_key'), str):
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return data.get(self._config.get('id_key'), None)
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elif isinstance(self._config.get('id_key'), list):
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return '_'.join([str(data[key]) for key in self._config.get('id_key') if key in data])
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return '_'.join([
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str(data[key]) for key in self._config.get('id_key')
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if key in data
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])
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def print_all_keys(self):
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print("config keys:")
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print('config keys:')
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for key, value in self._config.items():
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print(f" - {key}: {value}")
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print(f' - {key}: {value}')
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config_wrapper = None
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def initialize_config(config_path):
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global config_wrapper
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config_wrapper = ConfigWrapper(config_path)
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def get_config_wrapper():
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global config_wrapper
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if config_wrapper is None:
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raise RuntimeError("ConfigWrapper not initialized. Call initialize_config first.")
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raise RuntimeError(
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'ConfigWrapper not initialized. Call initialize_config first.')
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return config_wrapper
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if __name__ == '__main__':
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config_path = 'config/config.yaml'
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initialize_config(config_path)
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data = {'idx': '50', 'step':21, 'question': 'Ciphertext: "17,156,4,54,213,17,23,84,228,54,281"\n\nPlease provide the decrypted answer, encapsulated in double square brackets. For example, the format should be: [[decrypted answer]].', 'answer': '[[P]]', 'category': 'Decryption', 'rule_id': '23', 'input': 'Ciphertext: "17,156,4,54,213,17,23,84,228,54,281"', 'steps_num': 23, 'description': 'For a number c=228 in the ciphertext:\nCalculate z = c^e mod n. Here ^ means multiplication.\nz is 80.\nBased on the decimal number represented by z, use the ascii code to find the corresponding letter as the plaintext letter p.\nPlease give the letter p in [[...]] format.\n', 'atom': 80}
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print(config_wrapper.get_id(data))
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data = {
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'idx':
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'50',
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'step':
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21,
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'question':
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'Ciphertext: "17,156,4,54,213,17,23,84,228,54,281"\n\n'
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'Please provide the decrypted answer, encapsulated in double square'
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' brackets. For example, the format should be: [[decrypted answer]].',
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'answer':
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'[[P]]',
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'category':
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'Decryption',
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'rule_id':
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'23',
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'input':
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'Ciphertext: "17,156,4,54,213,17,23,84,228,54,281"',
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'steps_num':
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23,
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'description':
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'For a number c=228 in the ciphertext:\n'
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'Calculate z = c^e mod n. Here ^ means multiplication.\nz is 80.'
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'\nBased on the decimal number represented by z, use the ascii '
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'code to find the corresponding letter as the plaintext letter p.'
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'\nPlease give the letter p in [[...]] format.\n',
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'atom':
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80,
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}
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print(config_wrapper.get_id(data))
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@ -1,36 +1,31 @@
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import json
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# flake8: noqa: W605
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import re
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import argparse
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import os
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from prettytable import PrettyTable
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import pandas as pd
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from tqdm import tqdm
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import timeout_decorator
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import multiprocessing
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import time
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from functools import partial
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@timeout_decorator.timeout(5) # 5 seconds timeout
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def safe_regex_search(pattern, text, flags=0):
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try:
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return re.search(pattern, text, flags)
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except timeout_decorator.TimeoutError:
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print(f"Regex match timeout: pattern={pattern}, text={text[:100]}...")
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print(f'Regex match timeout: pattern={pattern}, text={text[:100]}...')
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return None
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except Exception as e:
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print(f"Regex match error: {str(e)}")
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print(f'Regex match error: {str(e)}')
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return None
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def extract_option_labels(text, options='ABCDEFGHIJ'):
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if not isinstance(text, str) or not isinstance(options, str):
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return 'error'
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text = text.rstrip()
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last_line = text.split('\n')[-1]
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|
||||
option_str = ''.join([chr(65 + i) for i in range(len(options))]) if options else 'ABCDEFGHIJ'
|
||||
|
||||
|
||||
option_str = ''.join([chr(65 + i) for i in range(len(options))
|
||||
]) if options else 'ABCDEFGHIJ'
|
||||
|
||||
patterns = [
|
||||
# e.g. "The final answer to this question is: A."
|
||||
# "The best option is $\boxed{B}:"
|
||||
@ -41,7 +36,7 @@ def extract_option_labels(text, options='ABCDEFGHIJ'):
|
||||
# "Answer: $\boxed{B}."
|
||||
# "ANSWER: (C):"
|
||||
f'(?i:Answer)[\*\s]*:\s*(?:[\*\$\\{{(\[\\\\(]*?(?:(?:\\\\boxed|\\\\mathbf|\\\\mathrm|\\\\text){{)?)*\s*([{option_str}])(?:\\\\?\}}?\$?\)?\]?\}}?)*(?:[\s:\.\*)]|$)',
|
||||
|
||||
|
||||
# e.g. "A"
|
||||
# "$\boxed{B}$"
|
||||
# "(C)."
|
||||
@ -53,46 +48,49 @@ def extract_option_labels(text, options='ABCDEFGHIJ'):
|
||||
match = safe_regex_search(pattern, last_line, re.IGNORECASE)
|
||||
if match:
|
||||
return match.group(1)
|
||||
|
||||
|
||||
for pattern in patterns:
|
||||
match = safe_regex_search(pattern, text, re.IGNORECASE)
|
||||
if match:
|
||||
return match.group(1)
|
||||
|
||||
|
||||
return None
|
||||
|
||||
|
||||
def extract_option_content(text, options_content=None):
|
||||
if not isinstance(text, str) or not isinstance(options_content, list):
|
||||
return 'error'
|
||||
|
||||
escaped_options_content = [re.escape(option_content) for option_content in options_content]
|
||||
|
||||
escaped_options_content = [
|
||||
re.escape(option_content) for option_content in options_content
|
||||
]
|
||||
escaped_options_content_str = '|'.join(escaped_options_content)
|
||||
|
||||
|
||||
text = text.rstrip()
|
||||
last_line = text.split('\n')[-1]
|
||||
|
||||
|
||||
patterns = [
|
||||
f'[Tt]he\s+(?:\w+\s+)?(?:answer|option)(?:\w+\s+)?\s+is:?\s*(?:[\*\$\\{{\(\[\\\\(]*?(?:(?:\\\\boxed|\\\\mathbf|\\\\mathrm|\\\\text){{)?)*\s*({escaped_options_content_str})(?:\\\\?\}}?\$?\)?\]?\}}?)*(?:[\s:\.\*)]|$)',
|
||||
|
||||
f'(?i:Answer)\s*(?:[\*\$\\{{\(\[\\\\(]*?(?:(?:\\\\boxed|\\\\mathbf|\\\\mathrm|\\\\text){{)?)*\s*({escaped_options_content_str})(?:\\\\?\}}?\$?\)?\]?\}}?)*(?:[\s:\.\*)]|$)',
|
||||
|
||||
f'^[^\w\r\n]*(?:[\*\$\\{{\(\[\\\\(]*?(?:(?:\\\\boxed|\\\\mathbf|\\\\mathrm|\\\\text){{)?)*\s*({escaped_options_content_str})(?:\\\\?\}}?\$?\)?\]?\}}?)*(?:[\s:\.\*)]|$)',
|
||||
]
|
||||
|
||||
|
||||
for pattern in patterns:
|
||||
match = safe_regex_search(pattern, last_line)
|
||||
if match:
|
||||
if match.group(1) in escaped_options_content:
|
||||
return options_content[escaped_options_content.index(match.group(1))]
|
||||
return options_content[escaped_options_content.index(
|
||||
match.group(1))]
|
||||
else:
|
||||
return match.group(1)
|
||||
|
||||
|
||||
for pattern in patterns:
|
||||
match = safe_regex_search(pattern, text)
|
||||
if match:
|
||||
if match.group(1) in escaped_options_content:
|
||||
return options_content[escaped_options_content.index(match.group(1))]
|
||||
return options_content[escaped_options_content.index(
|
||||
match.group(1))]
|
||||
else:
|
||||
return match.group(1)
|
||||
|
||||
|
||||
return None
|
||||
|
||||
@ -6,6 +6,7 @@ import sympy as sp
|
||||
import yaml
|
||||
from sympy.parsing.latex import parse_latex
|
||||
|
||||
|
||||
def load_yaml(yaml_path):
|
||||
"""Load a YAML file."""
|
||||
if not os.path.exists(yaml_path):
|
||||
@ -670,8 +671,7 @@ def evaluate_responses(data, mode, base_path=None):
|
||||
answer = record.get('gold', '')
|
||||
rule_id = record.get('rule_id', '')
|
||||
is_correct = evaluate_response_vs_answer(response, answer,
|
||||
question_type, rule_id,
|
||||
idx)
|
||||
question_type, rule_id, idx)
|
||||
result_dict = {
|
||||
'idx': idx,
|
||||
'response': response,
|
||||
@ -681,8 +681,10 @@ def evaluate_responses(data, mode, base_path=None):
|
||||
}
|
||||
if question_type == 'counterfactual':
|
||||
real_life_answer = record.get('real_life_answer', '')
|
||||
is_real_life = evaluate_response_vs_answer(
|
||||
response, real_life_answer, question_type, rule_id, idx)
|
||||
is_real_life = evaluate_response_vs_answer(response,
|
||||
real_life_answer,
|
||||
question_type, rule_id,
|
||||
idx)
|
||||
result_dict['real_life_answer'] = real_life_answer
|
||||
result_dict['is_real_life'] = is_real_life
|
||||
if question_type == 'cipher' and mode == 'subquestions':
|
||||
|
||||
@ -47,9 +47,8 @@ class BaseEvaluator:
|
||||
# please see opencompass/opencompass/tasks/openicl_eval.py Line 197-200
|
||||
return self._out_dir
|
||||
|
||||
def group(
|
||||
self, n: int, details: List[Dict[str, Any]], test_set: Dataset
|
||||
) -> Dict[str, Any]:
|
||||
def group(self, n: int, details: List[Dict[str, Any]],
|
||||
test_set: Dataset) -> Dict[str, Any]:
|
||||
example2replications = {}
|
||||
for detail, example in zip(details, test_set):
|
||||
example_abbr = f"{example['subdivision']}_{example['idx']}"
|
||||
@ -64,28 +63,23 @@ class BaseEvaluator:
|
||||
def reduce(self, details: List[Dict[str, Any]]) -> Dict[str, Any]:
|
||||
g_passk_details = OrderedDict()
|
||||
all_subdivisions = set(
|
||||
[detail['example_abbr'].split('_')[0] for detail in details]
|
||||
)
|
||||
[detail['example_abbr'].split('_')[0] for detail in details])
|
||||
all_metrics = list(details[0].keys())
|
||||
|
||||
for subdivision in sorted(list(all_subdivisions)):
|
||||
for metric in all_metrics:
|
||||
if metric in ['predictions', 'example_abbr']:
|
||||
continue
|
||||
g_passk_details[f'{subdivision}/{metric}'] = 100 * np.mean(
|
||||
[
|
||||
detail[metric]
|
||||
for detail in details
|
||||
if detail['example_abbr'].split('_')[0] == subdivision
|
||||
]
|
||||
)
|
||||
g_passk_details[f'{subdivision}/{metric}'] = 100 * np.mean([
|
||||
detail[metric] for detail in details
|
||||
if detail['example_abbr'].split('_')[0] == subdivision
|
||||
])
|
||||
|
||||
for metric in all_metrics:
|
||||
if metric in ['predictions', 'example_abbr']:
|
||||
continue
|
||||
g_passk_details[metric] = 100.0 * np.mean(
|
||||
[detail[metric] for detail in details]
|
||||
)
|
||||
[detail[metric] for detail in details])
|
||||
return g_passk_details
|
||||
|
||||
def evaluate(
|
||||
@ -104,7 +98,7 @@ class BaseEvaluator:
|
||||
if isinstance(x, Dataset):
|
||||
return x.select(range(i * real_size, (i + 1) * real_size))
|
||||
elif isinstance(x, Iterable):
|
||||
return x[i * real_size : (i + 1) * real_size]
|
||||
return x[i * real_size:(i + 1) * real_size]
|
||||
else:
|
||||
return x
|
||||
|
||||
@ -112,8 +106,7 @@ class BaseEvaluator:
|
||||
**{
|
||||
key: select_fn(i, real_size, value)
|
||||
for key, value in score_kwargs.items()
|
||||
}
|
||||
)
|
||||
})
|
||||
details = results.pop('details', None)
|
||||
if details is not None:
|
||||
if isinstance(details, Dict):
|
||||
@ -129,12 +122,10 @@ class BaseEvaluator:
|
||||
eval_results[key].append(single_results[key])
|
||||
for key in deepcopy(eval_results):
|
||||
if isinstance(eval_results[key][0], float) or isinstance(
|
||||
eval_results[key][0], int
|
||||
):
|
||||
eval_results[key][0], int):
|
||||
if n > 1:
|
||||
eval_results[key + f' ({n} runs average)'] = np.mean(
|
||||
eval_results[key]
|
||||
)
|
||||
eval_results[key])
|
||||
eval_results.pop(key)
|
||||
else:
|
||||
eval_results[key] = np.mean(eval_results[key])
|
||||
@ -163,22 +154,23 @@ class BaseEvaluator:
|
||||
thresholds = [0.0, 0.25, 0.5, 0.75, 1.0]
|
||||
for _k in [k] if isinstance(k, int) else k:
|
||||
for threshold in thresholds:
|
||||
g_pass = compute_g_pass_at_k(
|
||||
n=n, c=c, k=_k, t=threshold
|
||||
)
|
||||
g_pass = compute_g_pass_at_k(n=n,
|
||||
c=c,
|
||||
k=_k,
|
||||
t=threshold)
|
||||
detail[f'G-Pass@{_k}_{threshold}'] = g_pass
|
||||
detail[f'mG-Pass@{_k}'] = compute_mg_pass_at_k(
|
||||
n=n, c=c, k=_k
|
||||
)
|
||||
detail[f'mG-Pass@{_k}'] = compute_mg_pass_at_k(n=n,
|
||||
c=c,
|
||||
k=_k)
|
||||
|
||||
eval_details.append(detail)
|
||||
|
||||
if can_calculate and n > 1 and k > 1:
|
||||
eval_results.update(self.reduce(eval_details))
|
||||
|
||||
|
||||
# Store eval_details in eval_results
|
||||
eval_results['details'] = eval_details
|
||||
|
||||
|
||||
# Process details to flatten the predictions
|
||||
for detail in eval_details:
|
||||
# Extract all prediction fields and flatten them
|
||||
@ -189,16 +181,17 @@ class BaseEvaluator:
|
||||
flattened_predictions[k] = [v]
|
||||
else:
|
||||
flattened_predictions[k].append(v)
|
||||
|
||||
|
||||
# Replace the predictions list with the flattened dictionary
|
||||
for k, v in flattened_predictions.items():
|
||||
detail[k] = v
|
||||
|
||||
|
||||
# Remove the original predictions field
|
||||
detail.pop('predictions')
|
||||
import ipdb; ipdb.set_trace()
|
||||
import ipdb
|
||||
ipdb.set_trace()
|
||||
return eval_results
|
||||
|
||||
|
||||
# If there are no details, return an empty dictionary
|
||||
return {}
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user