mirror of
https://gitcode.com/JianFeeeee/homeagent-sdk.git
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464 lines
18 KiB
Python
464 lines
18 KiB
Python
#!/usr/bin/env python3
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"""
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Geng 综合评估引擎 (Comprehensive Assessment Engine)
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=====================================================
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一键运行所有适用的检测模块，生成综合学术数据打假报告。
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支持的领域：
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- biomedical: 生物医学（Western blot, 流式, 动物实验）
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- chemistry: 化学（光谱, 产率, 催化）
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- physics: 物理/材料（性能曲线, 电学/力学）
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- social_science: 社会科学（问卷, 量表）
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- clinical: 临床医学（生存数据, 临床指标）
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- general: 通用（不指定领域）
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致敬"耿同学讲故事" — 用数据说话，让造假无所遁形。
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"""
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import argparse
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import sys
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import json
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import os
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import csv
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import time
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from datetime import datetime
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from pathlib import Path
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# 导入各检测模块
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from last_digit_test import last_digit_test
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from benford_test import benford_test
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from decimal_consistency_test import decimal_consistency_test
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from fixed_relation_test import fixed_relation_test
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from grim_test import grim_test_batch
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def load_csv_data(input_file, delimiter=','):
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"""加载CSV数据，返回列名和数据"""
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columns = {}
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with open(input_file, 'r', encoding='utf-8-sig') as f:
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reader = csv.DictReader(f, delimiter=delimiter)
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fieldnames = reader.fieldnames
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for row in reader:
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for col in fieldnames:
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if col not in columns:
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columns[col] = []
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columns[col].append(row[col].strip() if row[col] else '')
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return columns, fieldnames
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def identify_numeric_columns(columns):
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"""识别数值列"""
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numeric_cols = {}
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for col_name, values in columns.items():
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numeric_values = []
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for v in values:
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try:
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if v:
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float(v)
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numeric_values.append(v)
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except ValueError:
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continue
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# 至少50%的值是数值
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if len(numeric_values) >= len(values) * 0.5 and len(numeric_values) >= 5:
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numeric_cols[col_name] = numeric_values
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return numeric_cols
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def run_assessment(input_file, domain='general', output_dir=None, delimiter=','):
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"""
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执行综合评估
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Parameters
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----------
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input_file : str
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输入CSV文件
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domain : str
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研究领域
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output_dir : str
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输出目录
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delimiter : str
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CSV分隔符
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"""
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start_time = time.time()
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# 加载数据
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columns, fieldnames = load_csv_data(input_file, delimiter)
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numeric_cols = identify_numeric_columns(columns)
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if not numeric_cols:
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return {
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'status': 'error',
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'message': '未找到有效的数值列，请检查输入文件格式'
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}
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report = {
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'meta': {
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'tool': 'Geng Academic Data Fraud Detection Tool',
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'version': '1.0.0',
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'timestamp': datetime.now().isoformat(),
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'input_file': os.path.basename(input_file),
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'domain': domain,
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'n_columns': len(fieldnames),
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'n_numeric_columns': len(numeric_cols),
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'numeric_columns': list(numeric_cols.keys()),
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'n_rows': max(len(v) for v in columns.values()) if columns else 0
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},
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'module_results': {},
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'summary': {}
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}
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all_risk_scores = []
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# ==========================================
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# Module 1: 末位数字检测 (对每个数值列)
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# ==========================================
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print("🔍 执行末位数字检测...")
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last_digit_results = {}
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for col_name, values in numeric_cols.items():
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result = last_digit_test(values, method='all_digits')
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if result.get('status') == 'completed':
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last_digit_results[col_name] = result
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all_risk_scores.append(result.get('risk_score', 0))
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if last_digit_results:
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report['module_results']['last_digit_test'] = {
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'module_name': '末位数字检测',
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'description': '检测数据末位数字是否偏离均匀分布',
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'columns_tested': len(last_digit_results),
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'results': last_digit_results
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}
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# ==========================================
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# Module 2: 本福特定律检测 (对每个数值列)
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# ==========================================
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print("🔍 执行本福特定律检测...")
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benford_results = {}
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for col_name, values in numeric_cols.items():
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# 本福特定律适用于跨多个数量级的数据
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try:
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float_values = [float(v) for v in values if v]
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value_range = max(float_values) / (min(v for v in float_values if v > 0) + 1e-10)
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# 只对跨度超过1个数量级的列做本福特检测
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if value_range > 10:
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result = benford_test(values, order=1)
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if result.get('status') == 'completed':
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benford_results[col_name] = result
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all_risk_scores.append(result.get('risk_score', 0))
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except (ValueError, ZeroDivisionError):
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continue
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if benford_results:
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report['module_results']['benford_test'] = {
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'module_name': '本福特定律检测',
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'description': '检测首位数字是否符合Benford\'s Law',
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'columns_tested': len(benford_results),
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'results': benford_results
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}
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# ==========================================
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# Module 3: 小数位一致性检测 (对每个数值列)
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# ==========================================
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print("🔍 执行小数位一致性检测...")
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decimal_results = {}
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for col_name, values in numeric_cols.items():
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# 只检测包含小数的列
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has_decimal = any('.' in v for v in values if v)
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if has_decimal:
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result = decimal_consistency_test(values)
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if result.get('status') == 'completed':
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decimal_results[col_name] = result
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all_risk_scores.append(result.get('risk_score', 0))
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if decimal_results:
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report['module_results']['decimal_consistency_test'] = {
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'module_name': '小数位一致性检测',
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'description': '检测数据小数部分是否存在异常模式',
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'columns_tested': len(decimal_results),
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'results': decimal_results
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}
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# ==========================================
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# Module 4: 固定关系检测 (两两比较数值列)
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# ==========================================
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print("🔍 执行固定关系检测...")
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fixed_results = {}
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col_names = list(numeric_cols.keys())
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# 限制比较对数，避免组合爆炸
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max_pairs = min(10, len(col_names) * (len(col_names) - 1) // 2)
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pair_count = 0
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for i in range(len(col_names)):
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if pair_count >= max_pairs:
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break
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for j in range(i + 1, len(col_names)):
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if pair_count >= max_pairs:
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break
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col1_name = col_names[i]
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col2_name = col_names[j]
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# 确保两列长度一致且有足够数据
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vals1 = numeric_cols[col1_name]
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vals2 = numeric_cols[col2_name]
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# 配对：只取两列都有值的行
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paired_1, paired_2 = [], []
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for v1, v2 in zip(vals1, vals2):
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try:
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if v1 and v2:
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float(v1)
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float(v2)
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paired_1.append(v1)
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paired_2.append(v2)
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except ValueError:
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continue
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if len(paired_1) >= 5:
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result = fixed_relation_test(paired_1, paired_2, col1_name, col2_name)
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if result.get('status') == 'completed':
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pair_key = f"{col1_name} vs {col2_name}"
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fixed_results[pair_key] = result
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all_risk_scores.append(result.get('risk_score', 0))
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pair_count += 1
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if fixed_results:
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report['module_results']['fixed_relation_test'] = {
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'module_name': '固定关系检测',
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'description': '检测不同列数据间是否存在不自然的数学关系',
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'pairs_tested': len(fixed_results),
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'results': fixed_results
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}
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# ==========================================
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# 综合评分
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# ==========================================
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print("📊 生成综合评估...")
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if all_risk_scores:
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# 综合评分：取各模块最高分的加权平均
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max_score = max(all_risk_scores)
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mean_score = sum(all_risk_scores) / len(all_risk_scores)
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# 综合分 = 60% 最高分 + 40% 平均分
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overall_score = 0.6 * max_score + 0.4 * mean_score
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overall_score = min(100, overall_score)
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else:
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overall_score = 0
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# 统计各风险等级
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high_risk_modules = [s for s in all_risk_scores if s >= 70]
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medium_risk_modules = [s for s in all_risk_scores if 40 <= s < 70]
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low_risk_modules = [s for s in all_risk_scores if s < 40]
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if overall_score >= 75:
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overall_level = 'critical'
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overall_level_cn = '🔴 极高风险'
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overall_emoji = '🔴'
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elif overall_score >= 50:
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overall_level = 'high'
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overall_level_cn = '🟠 高风险'
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overall_emoji = '🟠'
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elif overall_score >= 25:
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overall_level = 'medium'
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overall_level_cn = '🟡 中风险'
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overall_emoji = '🟡'
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else:
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overall_level = 'low'
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overall_level_cn = '🟢 低风险'
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overall_emoji = '🟢'
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report['summary'] = {
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'overall_risk_score': round(float(overall_score), 1),
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'overall_risk_level': overall_level,
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'overall_risk_level_cn': overall_level_cn,
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'n_modules_run': len(report['module_results']),
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'n_tests_total': len(all_risk_scores),
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'n_high_risk': len(high_risk_modules),
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'n_medium_risk': len(medium_risk_modules),
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'n_low_risk': len(low_risk_modules),
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'execution_time_seconds': round(time.time() - start_time, 2),
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'conclusion': _generate_conclusion(overall_score, overall_level, report['module_results']),
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'recommendations': _generate_recommendations(overall_level, domain, report['module_results'])
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}
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# 保存报告
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if output_dir:
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os.makedirs(output_dir, exist_ok=True)
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report_path = os.path.join(output_dir, 'geng_assessment_report.json')
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with open(report_path, 'w', encoding='utf-8') as f:
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json.dump(report, f, ensure_ascii=False, indent=2)
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# 生成可读的 Markdown 报告
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md_path = os.path.join(output_dir, 'geng_assessment_report.md')
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with open(md_path, 'w', encoding='utf-8') as f:
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f.write(_generate_markdown_report(report))
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print(f"\n📄 JSON报告已保存至: {report_path}")
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print(f"📄 Markdown报告已保存至: {md_path}")
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return report
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def _generate_conclusion(score, level, modules):
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"""生成结论"""
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if level == 'critical':
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return (
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"⚠️ 综合评估显示数据存在系统性异常，多项检测指标显著偏离正常预期。"
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"强烈建议对原始实验数据进行全面核查。"
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"注意：本工具仅提供线索筛查，最终判定需要领域专家复核。"
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)
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elif level == 'high':
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return (
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"⚠️ 数据中发现多处可疑模式，部分指标显著异常。"
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"建议对标记为高风险的数据列进行重点核查。"
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)
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elif level == 'medium':
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return (
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"⚡ 数据存在一些轻微异常，但不足以构成造假的确证。"
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"可能是测量精度限制、数据处理方式等正常因素导致。"
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"建议结合论文方法学描述进行综合判断。"
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)
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else:
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return (
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"✅ 数据各项检测指标均在正常范围内，未发现明显的造假迹象。"
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"注意：通过检测不代表数据一定真实，某些高明的造假可能无法被统计方法捕获。"
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)
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def _generate_recommendations(level, domain, modules):
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"""生成建议"""
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recs = []
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if level in ('critical', 'high'):
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recs.append("核查原始实验记录和数据记录本")
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recs.append("验证数据是否来自独立实验")
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recs.append("联系通讯作者要求提供原始数据")
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if domain == 'biomedical':
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recs.append("检查Western blot原始图片和流式原始FCS文件")
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recs.append("考虑向期刊或机构提交正式质疑")
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elif level == 'medium':
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recs.append("仔细阅读论文方法学部分，确认数据采集方式")
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recs.append("检查是否存在合理的解释（如仪器精度限制）")
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recs.append("可考虑联系作者进行非正式沟通")
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else:
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recs.append("当前数据未发现明显异常")
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recs.append("可考虑对补充材料中的数据做进一步检测")
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return recs
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def _generate_markdown_report(report):
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"""生成Markdown格式报告"""
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meta = report['meta']
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summary = report['summary']
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md = []
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md.append("# 📋 Geng 学术数据打假检测报告\n")
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md.append(f"> 生成时间: {meta['timestamp']}")
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md.append(f"> 检测工具: {meta['tool']} v{meta['version']}")
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md.append('> 致敬"耿同学讲故事"\n')
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md.append("## 📊 综合评估结果\n")
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md.append(f"| 指标 | 结果 |")
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md.append(f"|------|------|")
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md.append(f"| **综合风险评分** | **{summary['overall_risk_score']}/100** |")
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md.append(f"| **风险等级** | {summary['overall_risk_level_cn']} |")
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md.append(f"| 输入文件 | {meta['input_file']} |")
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md.append(f"| 检测领域 | {meta['domain']} |")
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md.append(f"| 数值列数 | {meta['n_numeric_columns']} |")
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md.append(f"| 数据行数 | {meta['n_rows']} |")
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md.append(f"| 运行模块数 | {summary['n_modules_run']} |")
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md.append(f"| 检测总数 | {summary['n_tests_total']} |")
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md.append(f"| 高风险项 | {summary['n_high_risk']} |")
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md.append(f"| 执行耗时 | {summary['execution_time_seconds']}s |\n")
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md.append("## 📝 结论\n")
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md.append(f"{summary['conclusion']}\n")
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md.append("## 💡 建议\n")
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for i, rec in enumerate(summary['recommendations'], 1):
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md.append(f"{i}. {rec}")
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md.append("")
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md.append("## 🔬 各模块检测详情\n")
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for module_key, module_data in report['module_results'].items():
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md.append(f"### {module_data['module_name']}\n")
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md.append(f"_{module_data['description']}_\n")
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if 'columns_tested' in module_data:
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md.append(f"- 检测列数: {module_data['columns_tested']}")
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if 'pairs_tested' in module_data:
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md.append(f"- 检测对数: {module_data['pairs_tested']}")
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# 列出各列/对的风险评分
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results = module_data.get('results', {})
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if results:
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md.append(f"\n| 检测对象 | 风险评分 | 风险等级 | 说明 |")
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md.append(f"|----------|----------|----------|------|")
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for key, res in results.items():
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score = res.get('risk_score', 'N/A')
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level = res.get('risk_level', 'N/A')
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interp = res.get('interpretation', '')[:60]
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md.append(f"| {key} | {score} | {level} | {interp}... |")
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md.append("")
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md.append("---\n")
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md.append("## ⚠️ 重要声明\n")
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md.append("- 本工具仅用于辅助筛查，**不能作为造假的最终判定依据**")
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md.append("- 数据异常 ≠ 数据造假（可能是仪器校准、单位转换、排版错误等）")
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md.append("- 检测结果需要领域专家复核")
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md.append("- 使用本工具时应遵守学术伦理和法律法规\n")
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md.append("---\n")
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md.append('*Powered by Geng Skill — 致敬"耿同学讲故事"*')
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return "\n".join(md)
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def main():
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parser = argparse.ArgumentParser(
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description='Geng 综合评估引擎 - 一键运行所有检测模块'
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)
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parser.add_argument('--input', '-i', required=True, help='输入CSV文件路径')
|
||
parser.add_argument('--domain', default='general',
|
||
choices=['biomedical', 'chemistry', 'physics',
|
||
'social_science', 'clinical', 'general'],
|
||
help='研究领域')
|
||
parser.add_argument('--output', '-o', default='./report', help='输出目录')
|
||
parser.add_argument('--delimiter', '-d', default=',', help='CSV分隔符')
|
||
|
||
args = parser.parse_args()
|
||
|
||
if not os.path.isfile(args.input):
|
||
print(f"错误：文件不存在: {args.input}", file=sys.stderr)
|
||
sys.exit(1)
|
||
|
||
print(f"{'='*60}")
|
||
print(f" Geng 学术数据打假检测工具 v1.0.0")
|
||
print(' 致敬"耿同学讲故事" — 用数据说话，让造假无所遁形')
|
||
print(f"{'='*60}")
|
||
print(f"\n📁 输入文件: {args.input}")
|
||
print(f"🔬 检测领域: {args.domain}")
|
||
print(f"📂 输出目录: {args.output}\n")
|
||
|
||
report = run_assessment(args.input, args.domain, args.output, args.delimiter)
|
||
|
||
if report.get('status') == 'error':
|
||
print(f"\n❌ 错误: {report['message']}", file=sys.stderr)
|
||
sys.exit(1)
|
||
|
||
summary = report['summary']
|
||
print(f"\n{'='*60}")
|
||
print(f" 综合评估结果")
|
||
print(f"{'='*60}")
|
||
print(f"\n {summary['overall_risk_level_cn']}")
|
||
print(f" 综合风险评分: {summary['overall_risk_score']}/100")
|
||
print(f" 高风险项: {summary['n_high_risk']} | "
|
||
f"中风险项: {summary['n_medium_risk']} | "
|
||
f"低风险项: {summary['n_low_risk']}")
|
||
print(f"\n {summary['conclusion']}")
|
||
print(f"\n{'='*60}")
|
||
|
||
|
||
if __name__ == '__main__':
|
||
main()
|