| """Scalar baselines on the released splits. Run inside the downloaded repo.""" |
| import json |
| from pathlib import Path |
| import numpy as np |
|
|
| if __name__=="__main__": |
| root=Path(__file__).resolve().parent |
| info=json.loads((root/"baselines.json").read_text()); target=info["target"] |
| train=[json.loads(s) for s in (root/"train.jsonl").read_text().splitlines()] |
| test=[json.loads(s) for s in (root/"test.jsonl").read_text().splitlines()] |
| exclude={"id","split","hdf5","group","nodes","cells","parameter_sha256","relative_nodal_l2_error",target} |
| names=[k for k in train[0] if k not in exclude] |
| X=np.array([[r[k] for k in names] for r in train]); Xt=np.array([[r[k] for k in names] for r in test]) |
| y=np.array([r[target] for r in train]); yt=np.array([r[target] for r in test]) |
| mu=X.mean(0); s=X.std(0); X=(X-mu)/s; Xt=(Xt-mu)/s |
| idx=np.argmin(((Xt[:,None,:]-X[None,:,:])**2).sum(2),axis=1) |
| for name,pred in [("train_mean",np.full_like(yt,y.mean())),("nearest_neighbor",y[idx])]: |
| print(name,"relative L2",np.linalg.norm(pred-yt)/np.linalg.norm(yt),"MAE",np.mean(abs(pred-yt))) |
|
|
