| r"""dataset.py — windows the prepped tensors for training. Full-graph, one target
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| day per sample. SSL masking for pretraining. Loss is restricted to fold nodes by
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| the caller (spatial split -> supervise on train nodes only)."""
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| from __future__ import annotations
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| import json, pathlib, numpy as np, torch
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|
|
| class AtmosIQData:
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| def __init__(self, tdir, window=7, device="cpu"):
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| t = pathlib.Path(tdir); self.w = window; self.dev = device
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| self.X = torch.tensor(np.load(t/"features.npy"), dtype=torch.float32)
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| self.PH = torch.tensor(np.load(t/"physics.npy"), dtype=torch.float32)
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| self.Y = torch.tensor(np.load(t/"targets.npy"), dtype=torch.float32)
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| self.M = torch.tensor(np.load(t/"target_mask.npy"), dtype=torch.float32)
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| ed = np.load(t/"edges.npz")
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| self.ei = torch.tensor(ed["ei"], dtype=torch.long)
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| self.e_hat = torch.tensor(ed["e_hat"], dtype=torch.float32)
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| self.n_hat = torch.tensor(ed["n_hat"], dtype=torch.float32)
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| self.split = torch.tensor(np.load(t/"node_split.npy"), dtype=torch.long)
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| self.D, self.N, self.F = self.X.shape
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| self.feat_cols = json.load(open(t/"feat_cols.json"))
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|
|
| def target_days(self, fold=0):
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| """days t (>= window-1) that have >=1 labeled cell in `fold`."""
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| fold_nodes = (self.split == fold)
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| has = ((self.M.sum(-1) > 0) & fold_nodes.unsqueeze(0)).sum(-1)
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| return [t for t in range(self.w-1, self.D) if has[t] > 0]
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|
|
| def window(self, t):
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| sl = slice(t-self.w+1, t+1)
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| return dict(X=self.X[sl].to(self.dev), wu=self.PH[sl,:,0].to(self.dev),
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| wv=self.PH[sl,:,1].to(self.dev), blh=self.PH[sl,:,2].to(self.dev),
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| y=self.Y[t].to(self.dev), m=self.M[t].to(self.dev))
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|
|
| def edges(self):
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| return (self.ei.to(self.dev), self.e_hat.to(self.dev), self.n_hat.to(self.dev))
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|
|
| def ssl_corrupt(X, ratio=0.15):
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| """mask a fraction of (node,feature) entries to 0; return corrupted X + mask."""
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| fmask = (torch.rand_like(X[-1]) < ratio).float()
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| Xc = X.clone(); Xc[-1] = X[-1] * (1 - fmask)
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| return Xc, fmask |
