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https://gitcode.com/JianFeeeee/homeagent-sdk.git
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245 lines
7.9 KiB
Python
245 lines
7.9 KiB
Python
#!/usr/bin/env python3
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"""
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GRIM 测试 (Granularity-Related Inconsistency of Means)
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========================================================
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原理：对于整数取值的数据（如李克特量表1-5、年龄等），
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给定样本量 n，合法的平均值只能取有限集合中的值。
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如果报告的平均值不在合法集合中，则数据存在不一致性。
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例：n=25，数据取值为整数，则平均值只能是 k/25 的形式，
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小数部分只能是 .00, .04, .08, .12, ..., .96
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参考：Brown & Heathers (2017). The GRIM Test. SPPS.
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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 math
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from decimal import Decimal, ROUND_HALF_UP
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def grim_test_single(mean, n, decimals=2, scale_min=None, scale_max=None):
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"""
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对单个均值执行 GRIM 测试
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Parameters
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----------
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mean : float or str
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报告的平均值
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n : int
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样本量
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decimals : int
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报告的小数位数
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scale_min : int, optional
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量表最小值（用于范围检查）
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scale_max : int, optional
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量表最大值（用于范围检查）
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Returns
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-------
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dict : 检测结果
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"""
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try:
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mean_val = Decimal(str(mean))
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n = int(n)
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except (ValueError, TypeError) as e:
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return {'status': 'error', 'message': f'无效输入: {e}'}
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if n <= 0:
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return {'status': 'error', 'message': '样本量必须大于0'}
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# 范围检查
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if scale_min is not None and scale_max is not None:
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if float(mean_val) < scale_min or float(mean_val) > scale_max:
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return {
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'status': 'range_error',
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'message': f'均值 {mean} 超出量表范围 [{scale_min}, {scale_max}]',
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'consistent': False
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}
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# 计算总和 = mean * n
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total = mean_val * n
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# 对于整数取值数据，总和必须是整数
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# 考虑四舍五入误差：检查 total 是否足够接近某个整数
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granularity = Decimal(1) / Decimal(10 ** decimals)
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# 在四舍五入精度范围内检查
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# mean 可能是真实值四舍五入到 decimals 位的结果
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# 真实 mean 在 [mean - 0.5*granularity, mean + 0.5*granularity) 范围内
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lower_total = (mean_val - granularity / 2) * n
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upper_total = (mean_val + granularity / 2) * n
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# 检查这个范围内是否包含整数
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lower_int = math.ceil(float(lower_total))
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upper_int = math.floor(float(upper_total))
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consistent = lower_int <= upper_int
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# 计算最近的合法均值
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nearest_total = round(float(mean_val * n))
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nearest_mean = nearest_total / n
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# 格式化到指定小数位
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fmt = f"%.{decimals}f"
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nearest_mean_str = fmt % nearest_mean
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reported_mean_str = fmt % float(mean_val)
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result = {
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'reported_mean': str(mean),
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'sample_size': n,
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'decimals': decimals,
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'consistent': consistent,
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'computed_sum': float(mean_val * n),
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'nearest_valid_mean': nearest_mean_str,
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'difference': round(abs(float(mean_val) - nearest_mean), decimals + 2)
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}
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if scale_min is not None and scale_max is not None:
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result['scale_range'] = f"[{scale_min}, {scale_max}]"
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return result
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def grim_test_batch(items):
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"""
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批量 GRIM 测试
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Parameters
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----------
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items : list of dict
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每个字典包含 'mean', 'n', 可选 'decimals', 'label'
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Returns
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-------
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dict : 批量检测结果
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"""
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results = []
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n_inconsistent = 0
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for i, item in enumerate(items):
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mean = item.get('mean')
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n = item.get('n')
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decimals = item.get('decimals', 2)
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label = item.get('label', f'Item {i+1}')
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scale_min = item.get('scale_min')
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scale_max = item.get('scale_max')
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res = grim_test_single(mean, n, decimals, scale_min, scale_max)
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res['label'] = label
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results.append(res)
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if not res.get('consistent', True):
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n_inconsistent += 1
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# 整体评估
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total = len(results)
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inconsistency_rate = n_inconsistent / total if total > 0 else 0
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# 风险评分
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if inconsistency_rate > 0.5:
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risk_level = 'high'
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risk_score = 70 + inconsistency_rate * 30
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elif inconsistency_rate > 0.25:
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risk_level = 'medium-high'
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risk_score = 50 + inconsistency_rate * 40
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elif inconsistency_rate > 0:
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risk_level = 'medium'
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risk_score = 30 + inconsistency_rate * 40
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else:
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risk_level = 'low'
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risk_score = 0
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summary = {
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'test_name': 'GRIM Test (均值粒度一致性检验)',
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'status': 'completed',
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'total_items': total,
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'inconsistent_items': n_inconsistent,
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'inconsistency_rate': round(inconsistency_rate, 4),
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'risk_level': risk_level,
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'risk_score': round(float(risk_score), 1),
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'details': results,
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'interpretation': _interpret_grim(n_inconsistent, total, inconsistency_rate)
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}
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return summary
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def _interpret_grim(n_inconsistent, total, rate):
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"""生成可读的解释"""
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if n_inconsistent == 0:
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return f"✅ 全部 {total} 个均值通过 GRIM 检验，未发现数值不一致。"
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elif rate > 0.5:
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return (
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f"⚠️ {total} 个均值中有 {n_inconsistent} 个（{rate:.0%}）未通过 GRIM 检验。"
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f"超过半数均值与样本量不兼容，这是严重的数据不一致信号。"
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f"强烈建议核查原始数据。"
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)
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elif rate > 0.25:
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return (
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f"⚠️ {total} 个均值中有 {n_inconsistent} 个（{rate:.0%}）未通过 GRIM 检验。"
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f"建议仔细核查这些不一致的数据点。"
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)
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else:
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return (
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f"⚡ {total} 个均值中有 {n_inconsistent} 个（{rate:.0%}）未通过 GRIM 检验。"
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f"少量不一致可能是四舍五入方式不同导致，建议结合其他检测综合判断。"
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)
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def main():
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parser = argparse.ArgumentParser(
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description='GRIM测试 - 检测报告均值与样本量的一致性'
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)
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parser.add_argument('--mean', type=str, help='报告的平均值')
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parser.add_argument('--n', type=int, help='样本量')
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parser.add_argument('--decimals', type=int, default=2, help='小数位数')
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parser.add_argument('--scale', type=str, help='量表范围，如 "1-5"')
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parser.add_argument('--input', '-i', help='输入JSON文件（批量测试）')
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parser.add_argument('--output', '-o', help='输出JSON文件路径')
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args = parser.parse_args()
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scale_min, scale_max = None, None
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if args.scale:
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parts = args.scale.split('-')
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if len(parts) == 2:
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scale_min, scale_max = int(parts[0]), int(parts[1])
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if args.input:
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# 批量模式
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with open(args.input, 'r', encoding='utf-8') as f:
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items = json.load(f)
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result = grim_test_batch(items)
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elif args.mean and args.n:
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# 单项模式
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res = grim_test_single(args.mean, args.n, args.decimals, scale_min, scale_max)
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result = {
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'test_name': 'GRIM Test (均值粒度一致性检验)',
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'status': 'completed',
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'result': res,
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'interpretation': (
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f"✅ 均值 {args.mean} 与样本量 {args.n} 一致" if res.get('consistent')
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else f"⚠️ 均值 {args.mean} 与样本量 {args.n} 不一致！最近合法均值为 {res.get('nearest_valid_mean')}"
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)
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}
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else:
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parser.print_help()
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sys.exit(1)
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output_json = json.dumps(result, ensure_ascii=False, indent=2)
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if args.output:
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with open(args.output, 'w', encoding='utf-8') as f:
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f.write(output_json)
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print(f"结果已保存至: {args.output}")
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else:
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print(output_json)
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if __name__ == '__main__':
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main()
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