|
|
| """
|
| 数据预处理和诊断工具
|
| 用于检查和修复foundation model特征
|
| """
|
| import torch
|
| import numpy as np
|
| import pandas as pd
|
| from torch.utils.data import DataLoader
|
| from tqdm import tqdm
|
|
|
|
|
| def diagnose_dataset(dataset, dataloader, device='cuda'):
|
| """
|
| 诊断数据集的各种统计信息
|
| """
|
| print("=" * 80)
|
| print("数据集诊断报告")
|
| print("=" * 80)
|
|
|
|
|
| print(f"\n[1] 基本信息")
|
| print(f" 样本数量: {len(dataset)}")
|
|
|
|
|
| all_pre = []
|
| all_post = []
|
| all_survival = []
|
| all_events = []
|
|
|
| print(f"\n[2] 收集数据中...")
|
| for batch in tqdm(dataloader):
|
| all_pre.append(batch['pre_latent'])
|
| all_post.append(batch['post_latent'])
|
| all_survival.append(batch['survival_time'])
|
| all_events.append(batch['event_indicator'])
|
|
|
| all_pre = torch.cat(all_pre, dim=0)
|
| all_post = torch.cat(all_post, dim=0)
|
| all_survival = torch.cat(all_survival)
|
| all_events = torch.cat(all_events)
|
|
|
|
|
| print(f"\n[3] Latent特征统计")
|
| print(f" Pre-latent:")
|
| print(f" 均值: {all_pre.mean():.6f}")
|
| print(f" 标准差: {all_pre.std():.6f}")
|
| print(f" 最小值: {all_pre.min():.6f}")
|
| print(f" 最大值: {all_pre.max():.6f}")
|
| print(f" 范围: [{all_pre.min():.3f}, {all_pre.max():.3f}]")
|
|
|
| print(f"\n Post-latent:")
|
| print(f" 均值: {all_post.mean():.6f}")
|
| print(f" 标准差: {all_post.std():.6f}")
|
| print(f" 最小值: {all_post.min():.6f}")
|
| print(f" 最大值: {all_post.max():.6f}")
|
|
|
| print(f"\n Pre vs Post差异:")
|
| diff = (all_post - all_pre).abs()
|
| print(f" 平均L1差异: {diff.mean():.6f}")
|
| print(f" 最大差异: {diff.max():.6f}")
|
| print(f" 相似度: {1 - diff.mean()/all_pre.abs().mean():.2%}")
|
|
|
|
|
| print(f"\n[4] 生存数据统计")
|
| print(f" 生存时间:")
|
| print(f" 均值: {all_survival.mean():.2f} 天")
|
| print(f" 中位数: {all_survival.median():.2f} 天")
|
| print(f" 范围: [{all_survival.min():.1f}, {all_survival.max():.1f}]")
|
|
|
| print(f"\n 事件指示器:")
|
| print(f" 事件发生率: {all_events.float().mean():.2%}")
|
| print(f" 事件数量: {all_events.sum().item()}")
|
| print(f" 删失数量: {(1-all_events).sum().item()}")
|
|
|
|
|
| five_year_survived = ((all_survival > 1825) & (all_events == 0))
|
| print(f"\n 5年生存:")
|
| print(f" 5年生存人数: {five_year_survived.sum().item()}")
|
| print(f" 5年生存率: {five_year_survived.float().mean():.2%}")
|
| print(f" ⚠️ 5年内死亡: {((all_survival <= 1825) & (all_events == 1)).sum().item()}")
|
|
|
|
|
| print(f"\n[5] 潜在问题检测")
|
| issues = []
|
|
|
| if all_pre.std() < 0.01:
|
| issues.append("⚠️ Pre-latent标准差过小，特征可能未归一化")
|
|
|
| if all_pre.abs().max() > 100:
|
| issues.append("⚠️ Pre-latent数值范围过大，建议归一化")
|
|
|
| if diff.mean() < 0.01:
|
| issues.append("⚠️ Pre和Post几乎相同，模型难以学习")
|
|
|
| if all_events.float().mean() < 0.1 or all_events.float().mean() > 0.9:
|
| issues.append(f"⚠️ 事件发生率极端 ({all_events.float().mean():.2%})，可能导致训练不稳定")
|
|
|
| if five_year_survived.sum() < 10:
|
| issues.append("⚠️ 5年生存样本过少，AUC可能无法计算")
|
|
|
| if len(issues) == 0:
|
| print(" ✓ 未发现明显问题")
|
| else:
|
| for issue in issues:
|
| print(f" {issue}")
|
|
|
| print("\n" + "=" * 80)
|
|
|
| return {
|
| 'pre_mean': all_pre.mean().item(),
|
| 'pre_std': all_pre.std().item(),
|
| 'post_mean': all_post.mean().item(),
|
| 'post_std': all_post.std().item(),
|
| 'event_rate': all_events.float().mean().item(),
|
| 'five_year_survival_rate': five_year_survived.float().mean().item()
|
| }
|
|
|
|
|
| def normalize_latent_features(dataset, method='standardize'):
|
| """
|
| 归一化latent特征
|
|
|
| Args:
|
| dataset: GliomaAllPairsTextDataset
|
| method: 'standardize' (z-score) or 'minmax' (0-1)
|
|
|
| Returns:
|
| mean, std (for standardize) or min, max (for minmax)
|
| """
|
| print(f"计算归一化参数（方法: {method}）...")
|
|
|
| all_features = []
|
| for i in tqdm(range(len(dataset))):
|
| sample = dataset[i]
|
| all_features.append(sample['pre_latent'])
|
| all_features.append(sample['post_latent'])
|
|
|
| all_features = torch.stack(all_features, dim=0)
|
|
|
| if method == 'standardize':
|
| mean = all_features.mean(dim=(0, 1), keepdim=True)
|
| std = all_features.std(dim=(0, 1), keepdim=True)
|
| std = torch.clamp(std, min=1e-6)
|
|
|
| print(f"均值范围: [{mean.min():.4f}, {mean.max():.4f}]")
|
| print(f"标准差范围: [{std.min():.4f}, {std.max():.4f}]")
|
|
|
| return mean.squeeze(), std.squeeze()
|
|
|
| elif method == 'minmax':
|
| min_val = all_features.min(dim=0, keepdim=True)[0].min(dim=1, keepdim=True)[0]
|
| max_val = all_features.max(dim=0, keepdim=True)[0].max(dim=1, keepdim=True)[0]
|
|
|
| print(f"最小值: {min_val.min():.4f}")
|
| print(f"最大值: {max_val.max():.4f}")
|
|
|
| return min_val.squeeze(), max_val.squeeze()
|
|
|
|
|
| class NormalizedDataset(torch.utils.data.Dataset):
|
| """
|
| 带归一化的数据集包装器
|
| """
|
| def __init__(self, base_dataset, mean, std, method='standardize'):
|
| self.base_dataset = base_dataset
|
| self.mean = mean
|
| self.std = std
|
| self.method = method
|
|
|
| def __len__(self):
|
| return len(self.base_dataset)
|
|
|
| def __getitem__(self, idx):
|
| sample = self.base_dataset[idx]
|
|
|
|
|
| if self.method == 'standardize':
|
| sample['pre_latent'] = (sample['pre_latent'] - self.mean) / self.std
|
| sample['post_latent'] = (sample['post_latent'] - self.mean) / self.std
|
| elif self.method == 'minmax':
|
| sample['pre_latent'] = (sample['pre_latent'] - self.mean) / (self.std - self.mean + 1e-6)
|
| sample['post_latent'] = (sample['post_latent'] - self.mean) / (self.std - self.mean + 1e-6)
|
|
|
| return sample
|
|
|
|
|
|
|
| if __name__ == "__main__":
|
| from dataset.dataset_glioma_all_pairs_text import GliomaAllPairsTextDataset, Config
|
|
|
|
|
| cfg = Config(
|
| timeline_json="dataset/MU-Glioma-Post/mu_glioma_post_timeline_with_survival.json",
|
| features_csv="./dataset/features_output.csv",
|
| take_dims=767
|
| )
|
| dataset = GliomaAllPairsTextDataset(cfg)
|
| dataloader = DataLoader(dataset, batch_size=32, num_workers=0)
|
|
|
|
|
| stats = diagnose_dataset(dataset, dataloader)
|
|
|
|
|
| mean, std = normalize_latent_features(dataset, method='standardize')
|
| normalized_dataset = NormalizedDataset(dataset, mean, std)
|
|
|
| print("\n✓ 归一化完成！") |
