#!/usr/bin/env python3 """ 固定关系检测 (Fixed Relationship Detection) ============================================ 原理：两组独立实验数据之间不应存在恒定的差值、比值或线性关系。 如果不同实验条件下的数据存在固定的数学关系，暗示数据可能是 从单一数据源通过简单数学运算生成的，而非独立实验获得。 这是"耿同学"打假方法中的核心策略之一——他发现许多造假论文中 不同实验组的数据存在固定差值或固定比例关系。 致敬"耿同学讲故事" — 用数据说话，让造假无所遁形。 """ import argparse import sys import json import numpy as np from scipy import stats def detect_fixed_difference(col1, col2, tolerance=0.01): """检测两列数据是否存在固定差值""" differences = np.array(col2) - np.array(col1) if len(differences) < 3: return None # 计算差值的变异系数 mean_diff = np.mean(differences) std_diff = np.std(differences) if abs(mean_diff) < 1e-10: cv = float('inf') if std_diff > 0 else 0 else: cv = abs(std_diff / mean_diff) # 判断差值是否恒定（CV极小） is_fixed = cv < tolerance and std_diff < tolerance * abs(mean_diff + 1e-10) # 检查差值是否完全相同 unique_diffs = np.unique(np.round(differences, 6)) is_exact = len(unique_diffs) == 1 return { 'type': 'fixed_difference', 'type_cn': '固定差值', 'mean_difference': round(float(mean_diff), 6), 'std_difference': round(float(std_diff), 6), 'cv': round(float(cv), 6) if cv != float('inf') else 'inf', 'is_fixed': bool(is_fixed or is_exact), 'is_exact': bool(is_exact), 'n_unique_differences': int(len(unique_diffs)) } def detect_fixed_ratio(col1, col2, tolerance=0.01): """检测两列数据是否存在固定比值""" col1 = np.array(col1, dtype=float) col2 = np.array(col2, dtype=float) # 避免除以零 mask = col1 != 0 if np.sum(mask) < 3: return None ratios = col2[mask] / col1[mask] mean_ratio = np.mean(ratios) std_ratio = np.std(ratios) if abs(mean_ratio) < 1e-10: cv = float('inf') if std_ratio > 0 else 0 else: cv = abs(std_ratio / mean_ratio) is_fixed = cv < tolerance unique_ratios = np.unique(np.round(ratios, 6)) is_exact = len(unique_ratios) == 1 return { 'type': 'fixed_ratio', 'type_cn': '固定比值', 'mean_ratio': round(float(mean_ratio), 6), 'std_ratio': round(float(std_ratio), 6), 'cv': round(float(cv), 6) if cv != float('inf') else 'inf', 'is_fixed': bool(is_fixed or is_exact), 'is_exact': bool(is_exact), 'n_unique_ratios': int(len(unique_ratios)) } def detect_linear_relationship(col1, col2): """检测两列数据是否存在高度线性关系""" col1 = np.array(col1, dtype=float) col2 = np.array(col2, dtype=float) if len(col1) < 3: return None # 线性回归 slope, intercept, r_value, p_value, std_err = stats.linregress(col1, col2) r_squared = r_value ** 2 # 残差分析 predicted = slope * col1 + intercept residuals = col2 - predicted max_residual = np.max(np.abs(residuals)) mean_residual = np.mean(np.abs(residuals)) # R² 非常接近1且残差极小 is_suspicious = r_squared > 0.9999 and max_residual < 0.001 * np.std(col2) return { 'type': 'linear_relationship', 'type_cn': '线性关系', 'slope': round(float(slope), 6), 'intercept': round(float(intercept), 6), 'r_squared': round(float(r_squared), 8), 'p_value': float(p_value), 'max_residual': round(float(max_residual), 8), 'mean_residual': round(float(mean_residual), 8), 'is_suspicious': bool(is_suspicious) } def detect_decimal_pattern(col1, col2): """检测两列数据小数部分是否高度一致""" col1 = np.array(col1, dtype=float) col2 = np.array(col2, dtype=float) # 提取小数部分 dec1 = col1 - np.floor(col1) dec2 = col2 - np.floor(col2) # 检查小数部分是否一致 dec_diff = np.abs(dec1 - dec2) n_matching = np.sum(dec_diff < 0.001) match_rate = n_matching / len(col1) return { 'type': 'decimal_pattern', 'type_cn': '小数位一致性', 'n_matching_decimals': int(n_matching), 'match_rate': round(float(match_rate), 4), 'is_suspicious': match_rate > 0.8 } def fixed_relation_test(col1, col2, col1_name='Column A', col2_name='Column B'): """ 综合固定关系检测 Parameters ---------- col1 : list of float 第一列数据 col2 : list of float 第二列数据 Returns ------- dict : 检测结果 """ if len(col1) != len(col2): return {'status': 'error', 'message': '两列数据长度不一致'} if len(col1) < 3: return {'status': 'insufficient_data', 'message': '数据量不足，至少需要3个数据点'} col1 = [float(x) for x in col1] col2 = [float(x) for x in col2] # 执行各项检测 results = {} diff_result = detect_fixed_difference(col1, col2) if diff_result: results['fixed_difference'] = diff_result ratio_result = detect_fixed_ratio(col1, col2) if ratio_result: results['fixed_ratio'] = ratio_result linear_result = detect_linear_relationship(col1, col2) if linear_result: results['linear_relationship'] = linear_result decimal_result = detect_decimal_pattern(col1, col2) if decimal_result: results['decimal_pattern'] = decimal_result # 综合风险评估 n_suspicious = sum([ 1 for r in results.values() if r.get('is_fixed') or r.get('is_suspicious') ]) if n_suspicious >= 3: risk_level = 'high' risk_score = 85 elif n_suspicious == 2: risk_level = 'medium-high' risk_score = 65 elif n_suspicious == 1: risk_level = 'medium' risk_score = 45 else: risk_level = 'low' risk_score = 10 # 如果存在完全精确的固定关系，直接拉高风险 if any(r.get('is_exact') for r in results.values()): risk_level = 'high' risk_score = max(risk_score, 90) summary = { 'test_name': 'Fixed Relationship Detection (固定关系检测)', 'status': 'completed', 'n_data_points': len(col1), 'column_1': col1_name, 'column_2': col2_name, 'detections': results, 'n_suspicious_patterns': n_suspicious, 'risk_level': risk_level, 'risk_score': round(float(risk_score), 1), 'interpretation': _interpret_fixed_relation(results, n_suspicious, col1_name, col2_name) } return summary def _interpret_fixed_relation(results, n_suspicious, col1_name, col2_name): """生成可读的解释""" findings = [] if results.get('fixed_difference', {}).get('is_fixed'): d = results['fixed_difference'] findings.append(f"两列数据存在固定差值 {d['mean_difference']}") if results.get('fixed_ratio', {}).get('is_fixed'): r = results['fixed_ratio'] findings.append(f"两列数据存在固定比值 {r['mean_ratio']}") if results.get('linear_relationship', {}).get('is_suspicious'): l = results['linear_relationship'] findings.append(f"两列数据存在完美线性关系 (R² = {l['r_squared']})") if results.get('decimal_pattern', {}).get('is_suspicious'): p = results['decimal_pattern'] findings.append(f"两列数据小数部分高度一致 (匹配率 {p['match_rate']:.0%})") if not findings: return f"✅ {col1_name} 与 {col2_name} 之间未发现固定数学关系，数据看起来是独立的。" findings_str = "；".join(findings) return ( f"⚠️ {col1_name} 与 {col2_name} 之间发现以下可疑模式：{findings_str}。" f"独立实验数据通常不应存在如此精确的数学关系，建议核查数据是否来自独立实验。" ) def load_data(input_file, col1, col2, delimiter=','): """从CSV文件加载两列数据""" import csv data1, data2 = [], [] with open(input_file, 'r', encoding='utf-8-sig') as f: reader = csv.DictReader(f, delimiter=delimiter) for row in reader: try: v1 = float(row[col1].strip()) v2 = float(row[col2].strip()) data1.append(v1) data2.append(v2) except (ValueError, KeyError): continue return data1, data2 def main(): parser = argparse.ArgumentParser( description='固定关系检测 - 检测两组数据间是否存在不自然的数学关系' ) parser.add_argument('--input', '-i', required=True, help='输入CSV文件路径') parser.add_argument('--col1', required=True, help='第一列列名') parser.add_argument('--col2', required=True, help='第二列列名') parser.add_argument('--delimiter', '-d', default=',', help='CSV分隔符') parser.add_argument('--output', '-o', help='输出JSON文件路径') args = parser.parse_args() data1, data2 = load_data(args.input, args.col1, args.col2, args.delimiter) if not data1: print("错误：未能加载有效数据", file=sys.stderr) sys.exit(1) result = fixed_relation_test(data1, data2, args.col1, args.col2) output_json = json.dumps(result, ensure_ascii=False, indent=2) if args.output: with open(args.output, 'w', encoding='utf-8') as f: f.write(output_json) print(f"结果已保存至: {args.output}") else: print(output_json) if __name__ == '__main__': main()