File size: 4,101 Bytes
653040f | 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 | import numpy as np
import torch
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader
class SpatialSEAAD_Dataset(Dataset):
def __init__(self, X, y_class, y_subclass, y_supertype, batch_donor, spatial_coords, cps, confidence, meta_info):
self.X = torch.from_numpy(X).float()
self.y_class = torch.from_numpy(y_class).long()
self.y_subclass = torch.from_numpy(y_subclass).long()
self.y_supertype = torch.from_numpy(y_supertype).long()
self.batch_donor = torch.from_numpy(batch_donor).long()
self.spatial_coords = torch.from_numpy(spatial_coords).float()
self.cps = torch.from_numpy(cps).float()
self.confidence = torch.from_numpy(confidence).float()
self.num_c = meta_info.item().get('num_class')
self.num_sc = meta_info.item().get('num_subclass')
self.num_st = meta_info.item().get('num_supertype')
# Per-donor z-score normalized spatial coords (scaled to ~gene expression range)
self.spatial_zscore = self._zscore_spatial_per_donor(
spatial_coords, batch_donor, scale=2.0)
@staticmethod
def _zscore_spatial_per_donor(spatial, batch_donor, scale=2.0):
"""Z-score normalize spatial coords per donor, then scale."""
spatial_norm = np.zeros_like(spatial, dtype=np.float32)
for d in np.unique(batch_donor):
mask = batch_donor == d
s = spatial[mask]
mean = s.mean(axis=0)
std = s.std(axis=0) + 1e-8
spatial_norm[mask] = (s - mean) / std * scale
return torch.from_numpy(spatial_norm).float()
def __len__(self):
return self.X.shape[0]
def __getitem__(self, idx):
return {
'X': self.X[idx],
'spatial': self.spatial_coords[idx],
'spatial_zscore': self.spatial_zscore[idx],
'batch_id': self.batch_donor[idx],
'y_class': self.y_class[idx],
'y_subclass': self.y_subclass[idx],
'y_supertype': self.y_supertype[idx],
'cps': self.cps[idx],
'confidence': self.confidence[idx]
}
def build_dataloaders(npz_path, batch_size=256, seed=42, num_workers=8):
data = np.load(npz_path, allow_pickle=True)
# 提取所有特征
X_all, y_c_all, y_sc_all, y_st_all = data['X'], data['y_class'], data['y_subclass'], data['y_supertype']
batch_all, spatial_all = data['batch_donor'], data['spatial']
cps_all, confidence_all = data['cps'], data['y_supertype_confidence']
meta_info = data['meta']
unique_donors = np.unique(batch_all)
np.random.seed(seed)
np.random.shuffle(unique_donors)
test_donors = unique_donors[:6]
val_donors = unique_donors[6:9]
train_donors = unique_donors[9:]
mask_train = np.isin(batch_all, train_donors)
mask_val = np.isin(batch_all, val_donors)
mask_test = np.isin(batch_all, test_donors)
train_dataset = SpatialSEAAD_Dataset(
X_all[mask_train], y_c_all[mask_train], y_sc_all[mask_train], y_st_all[mask_train],
batch_all[mask_train], spatial_all[mask_train], cps_all[mask_train], confidence_all[mask_train], meta_info
)
val_dataset = SpatialSEAAD_Dataset(
X_all[mask_val], y_c_all[mask_val], y_sc_all[mask_val], y_st_all[mask_val],
batch_all[mask_val], spatial_all[mask_val], cps_all[mask_val], confidence_all[mask_val], meta_info
)
test_dataset = SpatialSEAAD_Dataset(
X_all[mask_test], y_c_all[mask_test], y_sc_all[mask_test], y_st_all[mask_test],
batch_all[mask_test], spatial_all[mask_test], cps_all[mask_test], confidence_all[mask_test], meta_info
)
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, drop_last=True, num_workers=num_workers)
val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=True, num_workers=num_workers)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False, num_workers=num_workers)
return train_loader, val_loader, test_loader |