| 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') |
|
|
| |
| 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) |
| |
| |
| 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 |
