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fced272 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 | # utils/data_preprocessing.py
"""
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)
# 1. 基本统计
print(f"\n[1] 基本信息")
print(f" 样本数量: {len(dataset)}")
# 2. 收集所有数据
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) # [N, 4, 767]
all_post = torch.cat(all_post, dim=0) # [N, 4, 767]
all_survival = torch.cat(all_survival) # [N]
all_events = torch.cat(all_events) # [N]
# 3. Latent特征统计
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%}")
# 4. 生存数据统计
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()}")
# 5. 5年生存统计
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()}")
# 6. 潜在问题检测
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) # [2N, 4, 767]
if method == 'standardize':
mean = all_features.mean(dim=(0, 1), keepdim=True) # [1, 1, 767]
std = all_features.std(dim=(0, 1), keepdim=True) # [1, 1, 767]
std = torch.clamp(std, min=1e-6) # 避免除0
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]
# 归一化
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
# 使用示例
if __name__ == "__main__":
from dataset.dataset_glioma_all_pairs_text import GliomaAllPairsTextDataset, Config
# 加载数据
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)
# 诊断
stats = diagnose_dataset(dataset, dataloader)
# 归一化
mean, std = normalize_latent_features(dataset, method='standardize')
normalized_dataset = NormalizedDataset(dataset, mean, std)
print("\n✓ 归一化完成！") |