| |
| """Observational B0/B1 baselines on fit_clean_v0.1. Not intervention ground truth.""" |
| from __future__ import annotations |
|
|
| import json |
| from pathlib import Path |
|
|
| import numpy as np |
| import pandas as pd |
|
|
| ROOT = Path(__file__).resolve().parents[1] |
| PARQUET = ROOT / "data" / "processed" / "fit_clean_v0.1.parquet" |
| OUT = ROOT / "artifacts" / "observational_baselines" |
|
|
|
|
| def grouped_split(df: pd.DataFrame, seed: int = 0): |
| rng = np.random.default_rng(seed) |
| people = df["person_sha256"].dropna().unique() |
| rng.shuffle(people) |
| n = len(people) |
| n_train = int(0.7 * n) |
| n_val = int(0.15 * n) |
| train_p = set(people[:n_train]) |
| val_p = set(people[n_train:n_train + n_val]) |
| test_p = set(people[n_train + n_val:]) |
| return ( |
| df[df["person_sha256"].isin(train_p)].copy(), |
| df[df["person_sha256"].isin(val_p)].copy(), |
| df[df["person_sha256"].isin(test_p)].copy(), |
| {"n_people": int(n), "n_train_people": len(train_p), "n_val_people": len(val_p), "n_test_people": len(test_p)}, |
| ) |
|
|
|
|
| def rmse(y, yhat): |
| y = np.asarray(y, dtype=float) |
| yhat = np.asarray(yhat, dtype=float) |
| return float(np.sqrt(np.mean((y - yhat) ** 2))) |
|
|
|
|
| def mae(y, yhat): |
| y = np.asarray(y, dtype=float) |
| yhat = np.asarray(yhat, dtype=float) |
| return float(np.mean(np.abs(y - yhat))) |
|
|
|
|
| def main() -> int: |
| OUT.mkdir(parents=True, exist_ok=True) |
| df = pd.read_parquet(PARQUET) |
| df = df[df["usable_for_fitground"] == True].copy() |
| df = df.dropna(subset=["body_bust_cm", "garment_bust_cm", "bust_ease_cm", "person_sha256"]) |
| train, val, test, split_info = grouped_split(df) |
| |
| median_ease = float(train["bust_ease_cm"].median()) |
| b0_test = test["body_bust_cm"] + median_ease |
| |
| feats = ["body_bust_cm", "body_waist_cm", "body_hips_cm", "body_height_cm"] |
| Xtr = np.column_stack([train[feats].to_numpy(dtype=float), np.ones(len(train))]) |
| ytr = train["garment_bust_cm"].to_numpy(dtype=float) |
| coef, *_ = np.linalg.lstsq(Xtr, ytr, rcond=None) |
| Xte = np.column_stack([test[feats].to_numpy(dtype=float), np.ones(len(test))]) |
| b1_test = Xte @ coef |
| yte = test["garment_bust_cm"].to_numpy(dtype=float) |
| payload = { |
| "task": "observational_forward_prediction_of_garment_bust_cm", |
| "warning": "This is NOT a controlled intervention. Images are not local. Do not treat as correction ground truth.", |
| "n_rows_usable": int(len(df)), |
| "split": split_info, |
| "split_unit": "person_sha256", |
| "B0": { |
| "rule": "garment_bust = body_bust + median_train_ease", |
| "median_train_ease_cm": median_ease, |
| "test_rmse_cm": rmse(yte, b0_test), |
| "test_mae_cm": mae(yte, b0_test), |
| }, |
| "B1": { |
| "rule": "OLS garment_bust ~ body_bust + body_waist + body_hips + body_height", |
| "coef": {name: float(c) for name, c in zip(feats + ["intercept"], coef)}, |
| "test_rmse_cm": rmse(yte, b1_test), |
| "test_mae_cm": mae(yte, b1_test), |
| }, |
| "B2_vision": {"status": "NOT_RUN", "reason": "fit_clean image binaries are not on disk; only path refs to FIT-100K"}, |
| "B3_multimodal": {"status": "NOT_RUN", "reason": "no local images; not justified to download FIT-100K"}, |
| "intervention_regret": "NOT_RUN", |
| "simulation_verified_first_pass_correction_rate": "NOT_RUN", |
| "n_train": int(len(train)), |
| "n_val": int(len(val)), |
| "n_test": int(len(test)), |
| } |
| (OUT / "observational_baselines.json").write_text(json.dumps(payload, indent=2), encoding="utf-8") |
| print(json.dumps(payload, indent=2)) |
| return 0 |
|
|
|
|
| if __name__ == "__main__": |
| raise SystemExit(main()) |
|
|
