| """Reference methods written into the nnlab prediction store, so every new model |
| can be compared and ensembled with them draw by draw. |
| zs_lgbm zero-shot LightGBM (saved model, raw 393 features) |
| zs_xgb_dann teammate's FINAL before smoothing: (XGBoost + mean of 3 DANN seeds) / 2 |
| zs_dann teammate's DANN alone (mean of 3 seeds) |
| lgbm_upw our retrained LightGBM (Madrid + shots x30), one fit per draw |
| """ |
| import sys, time |
| from pathlib import Path |
| sys.path.insert(0, str(Path(__file__).resolve().parent)) |
| import lab |
| import numpy as np |
| W = lab.W |
|
|
| d = W.load_data() |
| ld = lab.load(official60=False) |
| assert (d["y_ams"] == ld["_ya"]).all() and np.allclose(d["coords_ams"], ld["pa"]) |
| if not lab.have_pred("zs_lgbm", 0, 0): |
| lab.save_pred("zs_lgbm", 0, 0, W.predict(W.load_zero_shot(), d["X_ams"])) |
| z = np.load(lab.PROC / "zeroshot_xgb_dann_ams.npz") |
| assert (z["pa"] == ld["pa"]).all() |
| Pd = z["P_dann_seeds"].mean(0) |
| lab.save_pred("zs_dann", 0, 0, Pd) |
| lab.save_pred("zs_xgb_dann", 0, 0, (z["P_xgb"] + Pd) / 2) |
| which = sys.argv[1] if len(sys.argv) > 1 else "all" |
| if which == "all": |
| for n, rep in lab.draws(): |
| if lab.have_pred("lgbm_upw", n, rep): |
| continue |
| t0 = time.time() |
| shot = lab.shots_for(ld, n, rep) |
| m, st = W.train_retrained(d["X_mad"], d["y_mad"], d["X_ams"], d["X_ams"][shot], lab.shot_labels(ld, shot)) |
| lab.save_pred("lgbm_upw", n, rep, W.predict_retrained(m, st, d["X_ams"])) |
| print(f"lgbm_upw n={n} rep={rep} {time.time()-t0:.0f}s", flush=True) |
| print("done") |
|
|
