编写质量筛选和难度筛选规则；
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506
layer2/PGEE/code/step4_high_quality.py
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506
layer2/PGEE/code/step4_high_quality.py
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import json
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import pandas as pd
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from typing import List, Dict, Any, Tuple
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from collections import defaultdict, Counter
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import numpy as np
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class QuestionFilterAndSelector:
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"""题目筛选和选择器 - 用于构建高质量评测集"""
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def __init__(self):
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# 各题型的最低难度阈值（排除过于简单的题目）
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self.min_difficulty_thresholds = {
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"calculation": 2, # 计算题：排除难度1的基础计算
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"short_answer": 2, # 简答题：排除难度1的简单记忆
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"true_false": 2, # 判断题：排除难度1的基础概念判断
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"multiple_choice": 2 # 选择题：排除难度1的简单选择
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}
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# 各知识层次的最低难度要求
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self.knowledge_level_min_difficulty = {
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"basic_concepts": 2, # 基础概念至少难度2（需要理解，不只是记忆）
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"simple_application": 2, # 简单应用至少难度2
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"medium_application": 2, # 中等应用至少难度2
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"complex_analysis": 3, # 复杂分析至少难度3
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"advanced_synthesis": 4 # 高级综合至少难度4
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}
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# 目标题库结构（百分比）
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self.target_distribution = {
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"question_types": {
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"calculation": 0.25, # 25% 计算题
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"short_answer": 0.45, # 45% 简答题
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"true_false": 0.15, # 15% 判断题
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"multiple_choice": 0.15 # 15% 选择题
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},
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"knowledge_levels": {
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"basic_concepts": 0.20, # 20% 基础概念（但要求难度2+）
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"simple_application": 0.25, # 25% 简单应用
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"medium_application": 0.30, # 30% 中等应用
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"complex_analysis": 0.20, # 20% 复杂分析
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"advanced_synthesis": 0.05 # 5% 高级综合
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},
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"difficulty_levels": {
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1: 0.05, # 5% 难度1（仅保留最有价值的）
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2: 0.25, # 25% 难度2
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3: 0.35, # 35% 难度3
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4: 0.25, # 25% 难度4
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5: 0.10 # 10% 难度5
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}
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}
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def filter_questions_by_quality(self, questions: List[Dict]) -> List[Dict]:
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"""第一步：按质量标准过滤题目"""
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filtered_questions = []
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for q in questions:
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# 基础质量检查
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if not self._is_valid_question(q):
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continue
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question_type = q.get('question_type', '')
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knowledge_level = q.get('knowledge_level', '')
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difficulty = q.get('difficulty', 0)
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# 应用题型最低难度阈值
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min_type_difficulty = self.min_difficulty_thresholds.get(question_type, 1)
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if difficulty < min_type_difficulty:
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continue
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# 应用知识层次最低难度要求
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min_level_difficulty = self.knowledge_level_min_difficulty.get(knowledge_level, 1)
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if difficulty < min_level_difficulty:
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continue
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# 特殊过滤规则
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if self._should_exclude_question(q):
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continue
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filtered_questions.append(q)
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return filtered_questions
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def _is_valid_question(self, q: Dict) -> bool:
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"""检查题目的基本有效性"""
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required_fields = ['question', 'answer', 'question_type', 'knowledge_level', 'difficulty']
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for field in required_fields:
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if not q.get(field):
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return False
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# 检查题目和答案长度（排除过短的）
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if len(q['question'].strip()) < 20: # 题目太短
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return False
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if len(q['answer'].strip()) < 5: # 答案太短
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return False
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return True
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def _should_exclude_question(self, q: Dict) -> bool:
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"""特殊排除规则"""
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question_type = q.get('question_type', '')
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knowledge_level = q.get('knowledge_level', '')
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difficulty = q.get('difficulty', 0)
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# 排除一些特定的低质量组合
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exclude_combinations = [
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# 基础概念+难度1的组合（纯记忆）
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(knowledge_level == 'basic_concepts' and difficulty == 1),
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# 选择题+基础概念+低难度的组合
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(question_type == 'multiple_choice' and
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knowledge_level == 'basic_concepts' and difficulty <= 2),
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# 判断题+基础概念+难度1的组合
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(question_type == 'true_false' and
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knowledge_level == 'basic_concepts' and difficulty == 1),
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]
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return any(exclude_combinations)
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def smart_sample_questions(self, filtered_questions: List[Dict],
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target_count: int = 2000) -> List[Dict]:
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"""智能抽样，保持分布平衡"""
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# 按类别分组
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grouped_questions = self._group_questions_by_categories(filtered_questions)
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# 计算目标数量
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target_counts = self._calculate_target_counts(target_count)
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# 分层抽样
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selected_questions = []
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# 1. 按题型分层抽样
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selected_by_type = self._stratified_sample_by_type(
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grouped_questions, target_counts, target_count
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)
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# 2. 在每个题型内按知识层次和难度平衡抽样
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final_selected = self._balance_within_types(selected_by_type, target_count)
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return final_selected
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def _group_questions_by_categories(self, questions: List[Dict]) -> Dict:
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"""按多个维度对题目分组"""
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grouped = {
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'by_type': defaultdict(list),
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'by_level': defaultdict(list),
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'by_difficulty': defaultdict(list),
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'by_type_level': defaultdict(lambda: defaultdict(list)),
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'by_type_difficulty': defaultdict(lambda: defaultdict(list))
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}
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for q in questions:
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qtype = q['question_type']
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level = q['knowledge_level']
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difficulty = q['difficulty']
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grouped['by_type'][qtype].append(q)
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grouped['by_level'][level].append(q)
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grouped['by_difficulty'][difficulty].append(q)
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grouped['by_type_level'][qtype][level].append(q)
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grouped['by_type_difficulty'][qtype][difficulty].append(q)
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return grouped
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def _calculate_target_counts(self, total_target: int) -> Dict:
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"""计算各类别的目标数量"""
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return {
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'by_type': {
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qtype: int(total_target * ratio)
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for qtype, ratio in self.target_distribution['question_types'].items()
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},
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'by_level': {
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level: int(total_target * ratio)
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for level, ratio in self.target_distribution['knowledge_levels'].items()
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},
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'by_difficulty': {
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diff: int(total_target * ratio)
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for diff, ratio in self.target_distribution['difficulty_levels'].items()
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}
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}
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def _stratified_sample_by_type(self, grouped_questions: Dict,
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target_counts: Dict, total_target: int) -> Dict:
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"""按题型分层抽样"""
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selected_by_type = {}
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for qtype, target_count in target_counts['by_type'].items():
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available_questions = grouped_questions['by_type'].get(qtype, [])
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if len(available_questions) <= target_count:
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# 如果可用题目不足，全部选择
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selected_by_type[qtype] = available_questions
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else:
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# 在该题型内进行智能抽样
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selected_by_type[qtype] = self._smart_sample_within_type(
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available_questions, target_count
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)
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return selected_by_type
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def _smart_sample_within_type(self, questions: List[Dict], target_count: int) -> List[Dict]:
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"""在单一题型内智能抽样"""
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# 按难度和知识层次分组
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by_difficulty = defaultdict(list)
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by_level = defaultdict(list)
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for q in questions:
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by_difficulty[q['difficulty']].append(q)
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by_level[q['knowledge_level']].append(q)
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selected = []
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# 优先选择高难度题目
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difficulty_priorities = [5, 4, 3, 2, 1]
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remaining_target = target_count
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for difficulty in difficulty_priorities:
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if remaining_target <= 0:
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break
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diff_questions = by_difficulty[difficulty]
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if not diff_questions:
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continue
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# 在该难度级别内按知识层次平衡选择
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level_groups = defaultdict(list)
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for q in diff_questions:
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level_groups[q['knowledge_level']].append(q)
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# 计算该难度级别应该选多少题
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target_for_this_diff = min(remaining_target,
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int(remaining_target * 0.4) if difficulty >= 4
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else int(remaining_target * 0.3))
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# 在各知识层次间分配
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selected_from_diff = self._distribute_across_levels(
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level_groups, target_for_this_diff
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)
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selected.extend(selected_from_diff)
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remaining_target -= len(selected_from_diff)
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# 如果还没达到目标数量，随机补充
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if len(selected) < target_count:
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remaining_questions = [q for q in questions if q not in selected]
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additional_needed = target_count - len(selected)
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if remaining_questions:
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import random
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additional = random.sample(
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remaining_questions,
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min(additional_needed, len(remaining_questions))
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)
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selected.extend(additional)
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return selected[:target_count]
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def _distribute_across_levels(self, level_groups: Dict, target_count: int) -> List[Dict]:
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"""在知识层次间分配题目"""
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if not level_groups or target_count <= 0:
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return []
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selected = []
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# 知识层次优先级（优先选择更高层次的）
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level_priorities = [
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'advanced_synthesis',
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'complex_analysis',
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'medium_application',
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'simple_application',
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'basic_concepts'
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]
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# 为每个层次分配配额
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level_quotas = {}
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remaining_target = target_count
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for level in level_priorities:
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if level not in level_groups or remaining_target <= 0:
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continue
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available_count = len(level_groups[level])
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if level in ['advanced_synthesis', 'complex_analysis']:
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quota = min(available_count, max(1, int(remaining_target * 0.4)))
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elif level == 'medium_application':
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quota = min(available_count, max(1, int(remaining_target * 0.3)))
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else:
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quota = min(available_count, max(1, int(remaining_target * 0.2)))
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level_quotas[level] = quota
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remaining_target -= quota
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# 按配额选择
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import random
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for level, quota in level_quotas.items():
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if quota > 0 and level in level_groups:
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sample_size = min(quota, len(level_groups[level]))
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selected.extend(random.sample(level_groups[level], sample_size))
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return selected
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def _balance_within_types(self, selected_by_type: Dict, target_count: int) -> List[Dict]:
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"""在题型选择结果间进行最终平衡"""
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all_selected = []
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for questions in selected_by_type.values():
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all_selected.extend(questions)
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# 如果总数超过目标，需要进一步筛选
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if len(all_selected) > target_count:
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# 按综合质量评分排序
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scored_questions = [(q, self._calculate_quality_score(q)) for q in all_selected]
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scored_questions.sort(key=lambda x: x[1], reverse=True)
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all_selected = [q for q, score in scored_questions[:target_count]]
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return all_selected
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def _calculate_quality_score(self, question: Dict) -> float:
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"""计算题目质量评分"""
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score = 0.0
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# 难度权重
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difficulty = question.get('difficulty', 1)
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score += difficulty * 2.0
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# 知识层次权重
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level_weights = {
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'basic_concepts': 1.0,
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'simple_application': 2.0,
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'medium_application': 3.0,
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'complex_analysis': 4.0,
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'advanced_synthesis': 5.0
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}
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score += level_weights.get(question.get('knowledge_level', ''), 1.0)
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# 题型权重（简答题和计算题更有价值）
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type_weights = {
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'short_answer': 2.0,
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'calculation': 2.0,
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'multiple_choice': 1.5,
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'true_false': 1.0
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}
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score += type_weights.get(question.get('question_type', ''), 1.0)
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# 题目长度权重（更详细的题目更有价值）
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question_length = len(question.get('question', ''))
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if question_length > 100:
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score += 1.0
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elif question_length > 200:
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score += 2.0
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return score
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def analyze_selection_results(self, original_questions: List[Dict],
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selected_questions: List[Dict]) -> Dict:
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"""分析选择结果"""
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def get_distribution(questions):
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total = len(questions)
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if total == 0:
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return {}
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dist = {
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'total': total,
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'by_type': Counter(q.get('question_type', '') for q in questions),
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'by_level': Counter(q.get('knowledge_level', '') for q in questions),
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'by_difficulty': Counter(q.get('difficulty', 0) for q in questions),
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'avg_difficulty': np.mean([q.get('difficulty', 0) for q in questions])
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}
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# 转换为百分比
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for key in ['by_type', 'by_level', 'by_difficulty']:
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dist[key + '_pct'] = {
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k: v/total*100 for k, v in dist[key].items()
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}
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return dist
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original_dist = get_distribution(original_questions)
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selected_dist = get_distribution(selected_questions)
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return {
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'original': original_dist,
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'selected': selected_dist,
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'selection_ratio': len(selected_questions) / len(original_questions) if original_questions else 0,
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'difficulty_improvement': selected_dist['avg_difficulty'] - original_dist['avg_difficulty']
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}
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def print_selection_report(self, analysis_results: Dict):
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"""打印选择报告"""
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print("\n" + "="*60)
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print("题目筛选结果报告")
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print("="*60)
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original = analysis_results['original']
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selected = analysis_results['selected']
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print(f"\n📊 基本统计:")
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print(f"原始题目数: {original['total']}")
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print(f"筛选后题目数: {selected['total']}")
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print(f"筛选比例: {analysis_results['selection_ratio']:.1%}")
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print(f"平均难度提升: {analysis_results['difficulty_improvement']:.2f}")
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print(f"\n📈 题型分布对比:")
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print(f"{'题型':<15} {'原始':<10} {'筛选后':<10} {'变化':<10}")
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print("-" * 50)
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for qtype in ['calculation', 'short_answer', 'true_false', 'multiple_choice']:
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orig_pct = original['by_type_pct'].get(qtype, 0)
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sel_pct = selected['by_type_pct'].get(qtype, 0)
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change = sel_pct - orig_pct
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print(f"{qtype:<15} {orig_pct:>7.1f}% {sel_pct:>8.1f}% {change:>+7.1f}%")
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print(f"\n🎯 难度分布对比:")
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print(f"{'难度':<8} {'原始':<10} {'筛选后':<10} {'变化':<10}")
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print("-" * 40)
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for diff in range(1, 6):
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orig_pct = original['by_difficulty_pct'].get(diff, 0)
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sel_pct = selected['by_difficulty_pct'].get(diff, 0)
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change = sel_pct - orig_pct
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print(f"难度{diff:<3} {orig_pct:>7.1f}% {sel_pct:>8.1f}% {change:>+7.1f}%")
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def main_filter_questions():
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"""主函数：筛选高质量题目"""
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# 文件路径
|
||||
INPUT_FILE = "/home/ubuntu/50T/LYT/MatBench/layer2/PGEE/code/step4_enhanced_classified_questions.json" # 分类后的题目文件
|
||||
OUTPUT_FILE = "/home/ubuntu/50T/LYT/MatBench/layer2/PGEE/code/step4_filtered_high_quality_questions.json" # 筛选后的输出文件
|
||||
ANALYSIS_FILE = "/home/ubuntu/50T/LYT/MatBench/layer2/PGEE/code/step4_selection_analysis.xlsx" # 分析报告
|
||||
|
||||
# 加载数据
|
||||
print("正在加载已分类的题目...")
|
||||
with open(INPUT_FILE, 'r', encoding='utf-8') as f:
|
||||
all_questions = json.load(f)
|
||||
|
||||
print(f"加载了 {len(all_questions)} 道题目")
|
||||
|
||||
# 初始化筛选器
|
||||
selector = QuestionFilterAndSelector()
|
||||
|
||||
# 第一步：质量过滤
|
||||
print("\n第一步：按质量标准过滤题目...")
|
||||
filtered_questions = selector.filter_questions_by_quality(all_questions)
|
||||
print(f"质量过滤后剩余: {len(filtered_questions)} 道题目")
|
||||
|
||||
# 第二步：智能抽样
|
||||
print("\n第二步：智能抽样构建评测集...")
|
||||
target_count = 2000 # 目标题目数
|
||||
selected_questions = selector.smart_sample_questions(filtered_questions, target_count)
|
||||
print(f"最终选择: {len(selected_questions)} 道题目")
|
||||
|
||||
# 保存结果
|
||||
print(f"\n保存筛选结果到: {OUTPUT_FILE}")
|
||||
with open(OUTPUT_FILE, 'w', encoding='utf-8') as f:
|
||||
json.dump(selected_questions, f, ensure_ascii=False, indent=2)
|
||||
|
||||
# 分析结果
|
||||
print("\n分析筛选结果...")
|
||||
analysis_results = selector.analyze_selection_results(all_questions, selected_questions)
|
||||
selector.print_selection_report(analysis_results)
|
||||
|
||||
# 导出详细分析
|
||||
try:
|
||||
df_original = pd.DataFrame(all_questions)
|
||||
df_selected = pd.DataFrame(selected_questions)
|
||||
|
||||
with pd.ExcelWriter(ANALYSIS_FILE, engine='openpyxl') as writer:
|
||||
df_selected.to_excel(writer, sheet_name='筛选结果', index=False)
|
||||
|
||||
# 统计对比
|
||||
comparison_data = []
|
||||
for metric in ['question_type', 'knowledge_level', 'difficulty']:
|
||||
orig_dist = df_original[metric].value_counts(normalize=True) * 100
|
||||
sel_dist = df_selected[metric].value_counts(normalize=True) * 100
|
||||
|
||||
for category in set(orig_dist.index) | set(sel_dist.index):
|
||||
comparison_data.append({
|
||||
'维度': metric,
|
||||
'类别': category,
|
||||
'原始占比': orig_dist.get(category, 0),
|
||||
'筛选后占比': sel_dist.get(category, 0),
|
||||
'变化': sel_dist.get(category, 0) - orig_dist.get(category, 0)
|
||||
})
|
||||
|
||||
pd.DataFrame(comparison_data).to_excel(writer, sheet_name='分布对比', index=False)
|
||||
|
||||
print(f"详细分析已保存到: {ANALYSIS_FILE}")
|
||||
|
||||
except ImportError:
|
||||
print("提示: 安装pandas和openpyxl可生成详细分析报告")
|
||||
|
||||
print(f"\n✅ 筛选完成！")
|
||||
print(f"🎯 最终评测集: {len(selected_questions)} 道高质量题目")
|
||||
print(f"📈 平均难度提升: {analysis_results['difficulty_improvement']:.2f}")
|
||||
print(f"💾 结果文件: {OUTPUT_FILE}")
|
||||
|
||||
if __name__ == "__main__":
|
||||
main_filter_questions()
|
||||
Reference in New Issue
Block a user