Conceptual rubric library & experiment metrics

P1 — Hardcoded label-parsing stride copied from submission record width

    # label format per record: x y z w l h yaw name  (8 fields)
    dims_by_class = {}
    for _, row in train_df.iterrows():
        tokens = str(row["LabelString"]).split()
        for i in range(0, len(tokens), 9):   # 9 = submission stride (conf first)
            rec = tokens[i:i+9]
            name = rec[8]                     # reads a numeric token, not a class
            dims_by_class.setdefault(name, []).append(
                [float(rec[4]), float(rec[5]), float(rec[6])])
    # later: submission written as "conf x y z w l h yaw name" (9 fields)

    Every record after the first is misaligned; `name` holds numeric strings, so
    dims_by_class contains no real class keys. Downstream lookups miss and fall
    back to default box sizes for all classes; training data contributes nothing
    and the competition metric drops by the full measured gap (1.0) versus
    correctly parsed labels.

    LABEL_FIELDS = 8  # x y z w l h yaw name — no confidence in ground truth
    KNOWN = {"LABEL_A", "LABEL_B"}
    tokens = str(row["LabelString"]).split()
    assert len(tokens) % LABEL_FIELDS == 0
    for i in range(0, len(tokens), LABEL_FIELDS):
        rec = tokens[i:i+LABEL_FIELDS]
        name = rec[7]
        if name in KNOWN:
            dims_by_class.setdefault(name, []).append(
                [float(rec[3]), float(rec[4]), float(rec[5])])

P1 — Label parsing with submission-format stride

    STRIDE = 9  # conf x y z w l h yaw class  (same layout as submission)
    dims = defaultdict(list)
    for s in train_df["gt_string"].fillna(""):
        t = s.split()
        for i in range(0, len(t) - STRIDE + 1, STRIDE):
            w, l, h = float(t[i+4]), float(t[i+5]), float(t[i+6])
            dims[t[i+8]].append((w, l, h))
    med = dims.get("LABEL_A") or [(1.9, 4.5, 1.7)]   # silent default
    box_w, box_l, box_h = np.median(np.array(med), axis=0)

    Label records actually have 8 fields (no confidence), so offsets drift by
    one field per record: width/length/height are read from position/yaw
    tokens and the "class" slot holds a numeric token. dims["LABEL_A"] stays
    empty, hardcoded defaults replace learned sizes, predicted box overlap
    (IoU) drops, and the mAP-based score falls (measured gap: full metric
    range, 1.0, versus the correctly parsed version). Exit code is 0.

    STRIDE = 8  # x y z w l h yaw class  (label layout, no confidence)
    dims = defaultdict(list)
    for s in train_df["gt_string"].fillna(""):
        t = s.split()
        assert len(t) % STRIDE == 0
        for i in range(0, len(t), STRIDE):
            cls = t[i+7]
            if cls.replace(".", "", 1).lstrip("-").isdigit():
                continue
            dims[cls].append((float(t[i+3]), float(t[i+4]), float(t[i+5])))

P1 — Label strings parsed with prediction-format field stride

    df = pd.read_csv("train.csv")
    dims_by_cls = {}
    for s in df["col_a"].fillna(""):
        toks = s.split()
        for i in range(0, len(toks), 9):      # 9 = prediction stride
            obj = toks[i:i+9]
            cls = obj[8]                       # actually yaw in label layout
            dims_by_cls.setdefault(cls, []).append(
                [float(obj[4]), float(obj[5]), float(obj[6])])

    Every class key is a numeric string like "-1.5708"; lookups for real class
    names return nothing, so hardcoded default box sizes are used for all
    detections. Detection metric drops sharply versus correct parsing
    (observed gap: 1.0 on the competition metric between the two variants).

    df = pd.read_csv("train.csv")
    dims_by_cls = {}
    for s in df["col_a"].fillna(""):
        toks = s.split()
        for i in range(0, len(toks), 8):      # 8 = ground-truth stride
            obj = toks[i:i+8]
            cls = obj[7]
            assert not cls.lstrip("-").replace(".", "").isdigit()
            dims_by_cls.setdefault(cls, []).append(
                [float(obj[3]), float(obj[4]), float(obj[5])])

P1 — Silent empty-prediction fallback on empty training set

    train_rows = []
    for rec in records:
        if rec['token'] in label_map:
            train_rows.append(build_features(rec))

    if len(train_rows) > 0:
        model.fit(stack(train_rows), targets)
        preds = [format_pred(model, r) for r in test_rows]
    else:
        preds = ['' for _ in test_rows]

    pd.DataFrame({'Id': ids, 'Pred': preds}).to_csv('submission.csv', index=False)

    A key mismatch in the assembly loop yields zero training rows; the run
    exits 0 and writes a valid-looking file of empty predictions, dropping
    the evaluation metric to its floor (0) instead of failing, and hiding
    the join bug from the operator.

    train_rows = []
    for rec in records:
        if rec['token'] in label_map:
            train_rows.append(build_features(rec))

    print("assembled training rows:", len(train_rows))
    if len(train_rows) == 0:
        raise RuntimeError("no training rows assembled — check join keys")

    model.fit(stack(train_rows), targets)
    preds = [format_pred(model, r) for r in test_rows]
    pd.DataFrame({'Id': ids, 'Pred': preds}).to_csv('submission.csv', index=False)

P1 — Silent empty-submission fallback on prediction failure

    if __name__ == "__main__":
        try:
            main()
        except Exception as e:
            print("FATAL:", repr(e))
            sub = pd.read_csv("sample_submission.csv")
            sub["col_a"] = ""
            sub.to_csv("submission.csv", index=False)
            print("wrote fallback empty submission")

    Any systematic bug in main() (e.g., a bad path or dtype) silently converts
    every prediction to an empty string; the run exits 0 in seconds and the
    metric collapses from its achievable value to the empty-submission
    baseline of 0.0, with no error surfaced.

    if __name__ == "__main__":
        try:
            main()
        except Exception as e:
            print("FATAL:", repr(e))
            raise
        sub = pd.read_csv("submission.csv")
        n_empty = (sub["col_a"].fillna("") == "").sum()
        assert n_empty < 0.5 * len(sub), "too many empty predictions"

P1 — Unverified template join with constant fill

    out = pd.DataFrame({"Id": test_ids, "Predicted": preds})
    # align to template ordering / ids
    s2 = template[["Id"]].merge(out, on="Id", how="left")
    s2["Predicted"] = s2["Predicted"].fillna(0).astype(int)
    s2.to_csv("submission.csv", index=False)

    template "Id" is string dtype while test_ids are integers, so zero rows
    match; every "Predicted" value becomes the constant 0. The file is written
    successfully and the run exits 0, but the class-averaged evaluation score
    drops from the model's actual level to the trivial baseline (~0).

    out = pd.DataFrame({"Id": test_ids, "Predicted": preds})
    s2 = template[["Id"]].merge(out, on="Id", how="left")
    matched = s2["Predicted"].notna().mean()
    if matched < 0.99:
        raise ValueError(f"join matched only {matched:.1%} of template rows")
    s2["Predicted"] = s2["Predicted"].fillna(0).astype(int)
    s2.to_csv("submission.csv", index=False)

P1 — Defaulting label lookup fabricates classes for unmatched ids

    labels_df = pd.read_csv("train.csv")
    label_map = dict(zip(labels_df["id"].astype(str), labels_df["LABEL_A"]))

    X, y = [], []
    for case_dir in sorted(os.listdir(train_dir)):
        feats = extract_features(case_dir)
        X.append(feats)
        y.append(label_map.get(str(int(case_dir)), 0))

    model.fit(np.array(X), np.array(y))

    Ids in the label file are zero-padded strings while the lookup key is
    de-padded via int(); every sample misses the map and receives label 0,
    so the fitted model predicts near-constant outputs and the ranking
    metric drops by ~0.15 versus a correctly joined label set, with exit
    code 0 and no warning emitted.

    labels_df = pd.read_csv("train.csv")
    labels_df["id"] = labels_df["id"].astype(str).str.zfill(5)
    ids = pd.DataFrame({"id": [str(d).zfill(5) for d in os.listdir(train_dir)]})
    joined = ids.merge(labels_df, on="id", how="left")
    assert joined["LABEL_A"].notna().all(), "missing labels for some ids"
    X = np.stack([extract_features(i) for i in joined["id"]])
    model.fit(X, joined["LABEL_A"].values)

P1 — Constant placeholder prediction string for every test row

    predictions = []
    for sample_id in sample_submission['Id'].tolist():
        if sample_id in test_lookup:
            # avoid false positives by predicting nothing
            pred_string = ""
        else:
            pred_string = ""
        predictions.append({'Id': sample_id, 'PredictionString': pred_string})
    pd.DataFrame(predictions).to_csv(OUT_PATH, index=False)

    Submission is entirely blank: metric collapses to the empty-baseline score
    (0.0 here), a full 1.0 below a script emitting crude prior-based guesses,
    despite all earlier fitting/statistics code running to completion.

    predictions = []
    for sample_id in sample_submission['Id'].tolist():
        if sample_id in test_lookup:
            pred_string = build_pred(stats, test_lookup[sample_id])
        else:
            pred_string = ""  # unmatched ids get no prediction
        predictions.append({'Id': sample_id, 'PredictionString': pred_string})
    pd.DataFrame(predictions).to_csv(OUT_PATH, index=False)

P1 — Silent empty-prediction fallback under broad exception handling

    rows = []
    for sid in sample_ids:
        try:
            pts = load_cloud(sid)
            pred = detect(pts)          # a bug here raises on every sample
        except Exception:
            pred = ""                   # silently substitute empty prediction
        rows.append((sid, pred))
    pd.DataFrame(rows, columns=["Id", "pred_col"]).to_csv("out.csv", index=False)

    Every iteration takes the fallback branch; the file contains only empty
    predictions and the metric collapses to the trivial baseline (0.0 vs the
    nonzero score of the working pipeline) while the run exits 0 in seconds.

    rows, n_fail = [], 0
    for sid in sample_ids:
        try:
            pred = detect(load_cloud(sid))
        except ValueError:              # narrow, expected case only
            pred = ""
            n_fail += 1
        rows.append((sid, pred))
    if n_fail > 0.05 * len(sample_ids):
        raise RuntimeError(f"{n_fail} fallbacks; refusing to write output")
    pd.DataFrame(rows, columns=["Id", "pred_col"]).to_csv("out.csv", index=False)

P1 — Defaulted label lookup fabricates training labels on key mismatch

    labels = dict(zip(df['id'], df['LABEL_A']))
    X, y = [], []
    for case_dir in sorted(train_dir.iterdir()):
        case_id = case_dir.name
        feats = extract_features(case_dir)
        X.append(feats)
        y.append(labels.get(case_id, 0))  # missing key -> fabricated label
    model.fit(np.array(X), np.array(y))

    Every identifier that fails to match (e.g. '00005' vs 5) is trained with
    label 0 regardless of its true class; the proportion of mislabeled rows
    grows with the mismatch rate, pushing held-out score toward chance —
    observed as a 0.109 drop in the evaluation metric versus a strict join,
    with the run completing cleanly and printing no warning.

    labels = dict(zip(df['id'], df['LABEL_A']))
    X, y = [], []
    for case_dir in sorted(train_dir.iterdir()):
        case_id = case_dir.name
        if case_id not in labels:
            continue
        X.append(extract_features(case_dir))
        y.append(labels[case_id])
    assert len(y) == len(df), "label join lost rows"
    model.fit(np.array(X), np.array(y))

P1 — Silent empty-prediction fallback on broad exception

    if __name__ == "__main__":
        try:
            main()
        except Exception as e:
            print("FATAL:", repr(e))
            sub = pd.read_csv("sample_submission.csv")
            sub["pred"] = ""
            sub.to_csv("submission.csv", index=False)
            print("wrote fallback empty submission")

    Any failure anywhere in main() silently yields a file of all-empty
    predictions; the evaluation metric collapses to the trivial baseline
    (score 0 vs. ~1.0 achievable) while the process exits with status 0.

    n_fail = 0
    preds = []
    for ex in examples:
        try:
            preds.append(predict_one(ex))
        except ValueError:
            n_fail += 1
            preds.append("")
    assert n_fail / len(examples) < 0.05, "too many empty predictions"
    df["pred"] = preds
    df.to_csv("submission.csv", index=False)

P1 — Unprobed metadata-joined paths with silent per-file fallback

    DATA = "/data/prepared/public"
    recs = json.load(open(os.path.join(DATA, "meta.json")))["images"]
    paths = [os.path.join(DATA, r["file_name"]) for r in recs]

    def feat(p):
        try:
            img = Image.open(p).convert("RGB").resize((32, 32))
            return np.asarray(img, dtype=np.float32).ravel()
        except Exception:
            return np.zeros(3072, dtype=np.float32)

    X = np.stack([feat(p) for p in paths])

    When the on-disk layout includes an extra subdirectory not reflected in the
    prefix, every open fails and is replaced by a zero vector; all rows of X are
    identical, predictions collapse to a single class, and the class-averaged
    evaluation metric falls to ~0 while the run still exits with status 0.

    DATA = "/data/prepared/public"
    recs = json.load(open(os.path.join(DATA, "meta.json")))["images"]
    paths = [os.path.join(DATA, r["file_name"]) for r in recs]
    if not os.path.exists(paths[0]):
        alt = os.path.join(DATA, "images")
        paths = [os.path.join(alt, r["file_name"]) for r in recs]
    assert sum(os.path.exists(p) for p in paths[:100]) > 90, "layout mismatch"

    def feat(p):
        img = Image.open(p).convert("RGB").resize((32, 32))
        return np.asarray(img, dtype=np.float32).ravel()

    X = np.stack([feat(p) for p in paths])

P1 — Blank-output fallback that masks fatal pipeline failure

    if __name__ == "__main__":
        try:
            main()
        except Exception as e:
            log("FATAL:", repr(e))
            sub = pd.read_csv("sample_submission.csv")
            sub["col_a"] = ""
            sub.to_csv("submission.csv", index=False)
            log("wrote fallback empty submission")

    A failure in any pipeline stage still yields a valid submission file and
    exit code 0; the evaluation metric is pinned at the trivial all-blank
    baseline (near the minimum of the score range) instead of the score real
    predictions would earn, and monitoring cannot distinguish the failed run
    from a successful one.

    if __name__ == "__main__":
        try:
            main()
        except Exception as e:
            log("FATAL:", repr(e))
            sub = pd.read_csv("sample_submission.csv")
            sub["col_a"] = ""
            sub.to_csv("submission_DEGRADED.csv", index=False)
            log("wrote degraded fallback artifact")
            sys.exit(1)

P1 — Half-precision reductions with artifact masking

    def load_and_process(ids, folder):
        feats = []
        for i in ids:
            arr = np.load(os.path.join(folder, i + ".npy"))  # stored float16
            feats.append([arr.mean(), arr.sum(), arr.std(), arr.max()])
        return np.array(feats)

    X = load_and_process(train_ids, train_dir)
    X = np.nan_to_num(X, nan=0.0, posinf=0.0, neginf=0.0)
    model.fit(scaler.fit_transform(X), y)

    Sum/std reductions overflow float16's ~65504 max to inf, nan_to_num zeroes
    them, and whole feature columns become constant zeros; held-out AUC drops
    from ~0.513 to ~0.492 (at or below chance) versus the float32-upcast run.

    def load_and_process(ids, folder):
        feats = []
        for i in ids:
            arr = np.load(os.path.join(folder, i + ".npy")).astype(np.float32)
            feats.append([arr.mean(), arr.sum(), arr.std(), arr.max()])
        return np.array(feats)

    X = load_and_process(train_ids, train_dir)
    X = np.nan_to_num(X, nan=0.0)  # only genuinely undefined values
    model.fit(scaler.fit_transform(X), y)

P1 — Constant zero-vector substituted for failed record decoding

    feats, labels = [], []
    for rec in records:
        try:
            img = Image.open(io.BytesIO(rec["payload"]))
            feats.append(extract(img))
        except Exception:
            feats.append(np.zeros(FEAT_DIM))
        labels.append(rec["label"])
    model.fit(np.array(feats), labels)
    preds = model.predict(np.array(test_feats))

    Every undecodable record becomes an identical all-zero row: training rows
    at the origin pull the fitted decision boundary toward an arbitrary class,
    and failed test records all receive whatever class the model assigns to
    the zero vector. Classification accuracy drops measurably (observed gap:
    0.33 on the task metric versus skipping failed records).

    feats, labels, failed_ids = [], [], []
    for rec in records:
        try:
            img = Image.open(io.BytesIO(rec["payload"]))
            feats.append(extract(img))
            labels.append(rec["label"])
        except Exception:
            failed_ids.append(rec["_id"])
            continue
    model.fit(np.array(feats), labels)
    fallback = Counter(labels).most_common(1)[0][0]

P1 — Uncertainty scaled from in-sample per-entity fit residuals

    conf = {}
    for pid, g in train.groupby("entity_id"):
        lr = LinearRegression().fit(g[["week"]], g["target"])
        resid = g["target"] - lr.predict(g[["week"]])
        conf[pid] = max(resid.std(), 50.0)

    sub["pred"] = sub["entity_id"].map(slopes_pred_fn)
    sub["confidence"] = sub["entity_id"].map(conf).fillna(150.0)
    sub.to_csv("submission.csv", index=False)

    Submitted sigma reflects smooth in-sample fit noise (~50-80 units) while
    actual forecast errors of the deployment path are several times larger;
    the negative-log-likelihood metric worsens on nearly every row where
    true error exceeds sigma, lowering the overall score by ~0.05.

    gkf = GroupKFold(n_splits=5)
    oof_err = np.zeros(len(tr))
    for tr_idx, va_idx in gkf.split(tr, groups=tr["entity_id"]):
        m = Ridge().fit(X[tr_idx], y[tr_idx])
        pred = base[va_idx] + m.predict(X[va_idx]) * dw[va_idx]
        oof_err[va_idx] = np.abs(tr["target"].values[va_idx] - pred)

    c0, c1 = fit_scale(oof_err, np.abs(dw))
    sub["confidence"] = np.clip(c0 + c1 * np.abs(sub["dw"]), 70, 1000)

P1 — Uncertainty calibrated on in-sample residuals

    sigmas = []
    for pid, g in train.groupby("entity_id"):
        m = LinearRegression().fit(g[["week"]], g["value"])
        resid = g["value"] - m.predict(g[["week"]])
        sigmas.append(resid.std())
    conf = float(np.nanmean(sigmas))
    sub["Confidence"] = conf
    sub.to_csv("submission.csv", index=False)

    Predicted uncertainty is systematically smaller than the true out-of-sample
    error, so a likelihood-based metric that penalizes overconfidence degrades
    (observed gap ~0.037 on the competition metric versus a calibration that
    did not reuse fitted rows).

    oof = cross_val_predict(
        model, X, y, cv=GroupKFold(5), groups=train["entity_id"]
    )
    resid = y - oof
    sub["Confidence"] = np.abs(resid).std()
    sub.to_csv("submission.csv", index=False)

P1 — Uncertainty parameter tuned on in-sample residuals

    model.fit(X_train, y_train)
    resid = y_train - model.predict(X_train)

    def nll(sigma):
        s = max(sigma, 70.0)
        d = np.minimum(np.abs(resid), 1000)
        return np.mean(np.sqrt(2) * d / s + np.log(np.sqrt(2) * s))

    best_sigma = minimize(nll, x0=[200.0]).x[0]
    sub["Confidence"] = best_sigma

    In-sample residuals understate true error, so the tuned sigma is
    systematically too small; the likelihood-style score on unseen rows
    degrades by roughly 0.2 absolute (~3% relative) versus a sigma tuned
    on held-out residuals.

    gkf = GroupKFold(n_splits=5)
    oof = np.zeros(len(y))
    for tr, va in gkf.split(X, y, groups=df["group_id"]):
        m = Ridge().fit(X[tr], y[tr])
        oof[va] = m.predict(X[va])
    resid = y - oof

    def nll(sigma):
        s = max(sigma, 70.0)
        d = np.minimum(np.abs(resid), 1000)
        return np.mean(np.sqrt(2) * d / s + np.log(np.sqrt(2) * s))

    best_sigma = minimize(nll, x0=[200.0]).x[0]

P1 — In-sample residuals used as submitted uncertainty scale

    model.fit(X_tr, y_tr)
    resid = y_tr - model.predict(X_tr)
    sigma = float(np.std(resid))

    preds = model.predict(X_te)
    out = pd.DataFrame({
        "id": test_ids,
        "value": preds,
        "confidence": sigma,
    })
    out.to_csv("submission.csv", index=False)

    In-sample residual spread understates held-out error, so the submitted
    confidence is too tight; the likelihood metric over-penalizes large
    errors and the final score drops (observed gap ~0.056 versus an
    out-of-fold calibration on the same features and model).

    oof = cross_val_predict(model, X_tr, y_tr,
                            cv=GroupKFold(5), groups=grp)
    resid = y_tr - oof
    sigma = float(np.std(resid))

    model.fit(X_tr, y_tr)
    out = pd.DataFrame({"id": test_ids,
                        "value": model.predict(X_te),
                        "confidence": sigma})
    out.to_csv("submission.csv", index=False)

P1 — Uncertainty scale tuned on in-sample residuals

    model.fit(X_train, y_train)
    preds = model.predict(X_train)
    resid = y_train - preds

    def neg_ll(sigma):
        s = np.maximum(sigma, 70)
        d = np.minimum(np.abs(resid), 1000)
        return np.mean(np.sqrt(2) * d / s + np.log(np.sqrt(2) * s))

    best_sigma = minimize(neg_ll, x0=[200], method="Nelder-Mead").x[0]
    sub["Confidence"] = best_sigma

    In-sample residuals are systematically smaller than held-out residuals, so the
    optimizer selects a sigma that is too small; on unseen rows the true errors exceed
    the optimistic scale and the log-likelihood metric degrades by ~2.5% relative
    versus tuning sigma on out-of-fold residuals.

    oof = np.zeros(len(train))
    gkf = GroupKFold(n_splits=5)
    for tr, va in gkf.split(X, y, groups=train["subject_id"]):
        m = Ridge().fit(X[tr], y[tr])
        oof[va] = m.predict(X[va])
    resid = y - oof

    def neg_ll(sigma):
        s = np.maximum(sigma, 70)
        d = np.minimum(np.abs(resid), 1000)
        return np.mean(np.sqrt(2) * d / s + np.log(np.sqrt(2) * s))

    best_sigma = minimize(neg_ll, x0=[200], method="Nelder-Mead").x[0]

P1 — Uncertainty from in-sample residuals of per-entity fits

    sigmas = {}
    for pid, g in train.groupby("entity_id"):
        coeffs = np.polyfit(g["week"], g["col_a"], 1)
        resid = g["col_a"] - np.polyval(coeffs, g["week"])
        sigmas[pid] = resid.std()

    sub["pred"] = sub["entity_id"].map(slopes_pred)
    sub["Confidence"] = sub["entity_id"].map(sigmas).fillna(150.0)
    sub.to_csv("submission.csv", index=False)

    Submitted sigma is systematically smaller than the true error on unseen
    weeks; the metric's |error|/sigma penalty term rises and the final score
    degrades by ~0.05 relative to an out-of-fold residual estimate.

    oof = np.zeros(len(train))
    for tr, va in GroupKFold(5).split(X, y, groups=train["entity_id"]):
        m = Ridge().fit(X[tr], y[tr])
        oof[va] = m.predict(X[va])
    resid = y - oof
    base_sigma = np.std(resid)
    sub["Confidence"] = base_sigma + 0.5 * np.abs(sub["dw"])
    sub.to_csv("submission.csv", index=False)

P1 — Uncertainty parameter tuned on in-sample residuals

    from scipy.optimize import minimize
    coef = np.linalg.lstsq(X_train, y_train, rcond=None)[0]
    preds = X_train @ coef
    resid = y_train - preds

    def neg_score(sigma):
        s = np.maximum(sigma, 70)
        d = np.minimum(np.abs(resid), 1000)
        return np.mean(np.sqrt(2) * d / s + np.log(np.sqrt(2) * s))

    best_sigma = minimize(neg_score, x0=200.0).x[0]
    submission["Confidence"] = best_sigma

    In-sample residuals understate the true error spread, so best_sigma is
    systematically too small; on unseen rows the likelihood metric applies a
    larger penalty per error unit, and the leaderboard score is measurably
    lower (~0.02 worse on the metric) than tuning on held-out residuals.

    from sklearn.model_selection import GroupKFold
    oof = np.zeros(len(y))
    for tr, va in GroupKFold(5).split(X, y, groups=subj):
        m = Ridge().fit(X[tr], y[tr])
        oof[va] = m.predict(X[va])
    resid = y - oof

    def neg_score(sigma):
        s = max(sigma, 70)
        return np.mean(np.sqrt(2) * np.minimum(np.abs(resid), 1000) / s
                       + np.log(np.sqrt(2) * s))
    best_sigma = minimize(neg_score, x0=200.0).x[0]

P1 — Zero-offset baseline rows included in residual calibration fit

    base = df.sort_values("Weeks").groupby("Patient").first().reset_index()
    df = df.merge(base[["Patient", "Weeks", "val"]].rename(
        columns={"Weeks": "base_week", "val": "base_val"}), on="Patient")
    df["dw"] = df["Weeks"] - df["base_week"]
    X = df[["dw", "base_val", "col_a"]].values
    y = df["val"].values
    model.fit(X, y)
    resid = y - model.predict(X)
    sigma = np.std(resid)          # deflated by zero-offset rows
    out["Confidence"] = sigma

    Zero-offset rows have target == base_val, so their residuals are near
    zero; the pooled residual std is biased low, the emitted confidence is
    systematically too small, and the held-out likelihood-based score
    degrades (observed ~0.029 worse on the competition metric).

    base = df.sort_values("Weeks").groupby("Patient").first().reset_index()
    df = df.merge(base[["Patient", "Weeks", "val"]].rename(
        columns={"Weeks": "base_week", "val": "base_val"}), on="Patient")
    df["dw"] = df["Weeks"] - df["base_week"]
    fit_df = df[df["dw"] != 0]
    X = fit_df[["dw", "base_val", "col_a"]].values
    y = fit_df["val"].values
    model.fit(X, y)
    resid = y - model.predict(X)
    sigma = np.std(resid)

P1 — Uncertainty parameter tuned on in-sample residuals

    model.fit(X_train, y_train)
    resid = y_train - model.predict(X_train)

    def neg_loglik(sigma):
        s = np.maximum(sigma, 70.0)
        d = np.minimum(np.abs(resid), 1000.0)
        return np.mean(np.sqrt(2) * d / s + np.log(np.sqrt(2) * s))

    best_sigma = minimize(neg_loglik, x0=[200.0]).x[0]
    sub["Confidence"] = best_sigma

    In-sample residuals are optimistically small, so the optimizer selects a
    sigma below the true predictive error; on unseen rows the clipped
    log-likelihood metric degrades (~2.8% relative worse than tuning the same
    sigma on out-of-fold residuals).

    oof = np.zeros(len(y))
    for tr, va in GroupKFold(5).split(X, y, groups):
        m = Ridge().fit(X[tr], y[tr])
        oof[va] = m.predict(X[va])
    resid = y - oof
    best_sigma = minimize(neg_loglik_from(resid), x0=[200.0]).x[0]
    model = Ridge().fit(X, y)
    sub["Confidence"] = best_sigma

P1 — Uncertainty scale from per-subject in-sample residuals mismatched to deployed feature-only model

    resids = []
    for pid, g in train.groupby("subject_id"):
        lr = LinearRegression().fit(g[["week"]], g["target"])
        resids.extend(g["target"] - lr.predict(g[["week"]]))
    sigma = np.std(resids)  # in-sample, per-subject history fits

    model = LinearRegression().fit(train[feature_cols], train["target"])
    sub["target"] = model.predict(sub[feature_cols])
    sub["Confidence"] = sigma
    sub.to_csv("submission.csv", index=False)

    sigma reflects tight per-subject trajectory fits, not the deployed
    feature-only model's true error; the confidence value is systematically
    too small, so the |error|/sigma penalty term is inflated on every row
    and the likelihood-style score drops (~0.028 worse on the metric than
    when the scale matches the deployed model).

    tr, va = train_test_split(train, test_size=0.2, random_state=0)
    model = LinearRegression().fit(tr[feature_cols], tr["target"])
    va_resid = va["target"] - model.predict(va[feature_cols])
    sigma = np.std(va_resid)  # held-out residuals of the deployed model

    model = LinearRegression().fit(train[feature_cols], train["target"])
    sub["target"] = model.predict(sub[feature_cols])
    sub["Confidence"] = sigma
    sub.to_csv("submission.csv", index=False)

P1 — Uncertainty scale calibrated from in-sample residuals

    confs = {}
    for eid, g in df.groupby("entity_id"):
        coef = np.polyfit(g["week"], g["target"], 1)
        pred = np.polyval(coef, g["week"])
        resid = g["target"] - pred          # residuals on the fitting rows
        confs[eid] = max(70.0, resid.std())
    sub["Confidence"] = sub["entity_id"].map(confs)
    sub.to_csv("submission.csv", index=False)

    Submitted uncertainty values understate the true forecast error at
    unseen weeks; the likelihood-style score degrades (e.g. ~0.04 worse)
    versus deriving the scale from held-out residuals.

    oof_resid = []
    for tr_idx, va_idx in GroupKFold(5).split(X, y, groups=df["entity_id"]):
        model.fit(X[tr_idx], y[tr_idx])
        oof_resid.append(y[va_idx] - model.predict(X[va_idx]))
    scale = np.abs(np.concatenate(oof_resid)).mean()
    sub["Confidence"] = np.maximum(70.0, scale * (1 + 0.02 * sub["week_gap"]))
    sub.to_csv("submission.csv", index=False)

P1 — Uncertainty parameter tuned on in-sample residuals

    model.fit(X_train, y_train)
    resid = y_train - model.predict(X_train)

    def neg_ll(sigma):
        s = max(sigma[0], 70.0)
        d = np.minimum(np.abs(resid), 1000)
        return np.mean(np.sqrt(2) * d / s + np.log(np.sqrt(2) * s))

    best_sigma = minimize(neg_ll, x0=[200.0]).x[0]
    sub_df["Confidence"] = best_sigma

    In-sample residuals understate true error, so the optimized sigma is
    systematically too small; on held-out entities the log-likelihood metric
    degrades by roughly 0.2 absolute (~3% relative) versus tuning on
    out-of-fold residuals.

    gkf = GroupKFold(n_splits=5)
    oof = np.zeros(len(y))
    for tr, va in gkf.split(X, y, groups=df["entity_id"]):
        m = Ridge().fit(X[tr], y[tr])
        oof[va] = m.predict(X[va])
    resid = y - oof
    best_sigma = minimize(neg_ll_from(resid), x0=[200.0]).x[0]
    sub_df["Confidence"] = best_sigma

P1 — Row-level random split across derived variants of the same source item

    items = sorted(os.listdir(src_dir))
    rows, labels = [], []
    for variant in ["orig", "mod_a", "mod_b"]:
        for name in items:
            rows.append(extract_features(os.path.join(src_dir, variant, name)))
            labels.append(0 if variant == "orig" else 1)
    X, y = np.vstack(rows), np.array(labels)
    Xtr, Xva, ytr, yva = train_test_split(X, y, test_size=0.2, random_state=0)
    model.fit(Xtr, ytr, eval_set=[(Xva, yva)], early_stopping_rounds=50)
    print("val score:", score(yva, model.predict(Xva)))

    Reported validation score is optimistically inflated (observed gap ~0.29 on
    the final metric vs. a group-level split): near-duplicate variants of the
    same item appear in both partitions, early stopping halts at an iteration
    tuned to leaked rows, and the held-out score comes in well below the
    validation number used for model selection.

    items = sorted(os.listdir(src_dir))
    tr_items, va_items = train_test_split(items, test_size=0.2, random_state=0)
    def build(subset):
        rows, labels = [], []
        for variant in ["orig", "mod_a", "mod_b"]:
            for name in subset:
                rows.append(extract_features(os.path.join(src_dir, variant, name)))
                labels.append(0 if variant == "orig" else 1)
        return np.vstack(rows), np.array(labels)
    Xtr, ytr = build(tr_items)
    Xva, yva = build(va_items)
    model.fit(Xtr, ytr, eval_set=[(Xva, yva)], early_stopping_rounds=50)

P1 — Unbounded linear trend extrapolation over the prediction horizon

    for pid, grp in train.groupby("entity_id"):
        slope = np.polyfit(grp["week"], grp["target"], 1)[0]
        slopes[pid] = slope

    for i, (pid, w) in enumerate(zip(entities, weeks)):
        base = baselines[pid]
        dw = w - base_week[pid]
        preds[i] = base + slopes[pid] * dw

    sub["target"] = preds
    sub.to_csv("submission.csv", index=False)

    Entities with two closely spaced noisy observations get extreme slope
    estimates; at horizon offsets of +/-100 steps the predictions leave the
    physically plausible target range, driving per-row absolute error to the
    metric's error cap on those rows and lowering the averaged score by ~0.05
    versus a bounded extrapolation.

    for pid, grp in train.groupby("entity_id"):
        slope = np.polyfit(grp["week"], grp["target"], 1)[0]
        slopes[pid] = np.clip(slope, -12, 2)

    for i, (pid, w) in enumerate(zip(entities, weeks)):
        dw = w - base_week[pid]
        preds[i] = baselines[pid] + slopes[pid] * dw

    lo, hi = train["target"].min(), train["target"].max()
    sub["target"] = np.clip(preds, lo, hi)
    sub.to_csv("submission.csv", index=False)

P1 — fixed per-class quota subsampling under accuracy scoring

    PER_CLASS = 40
    bucket = {}
    for rec in iter_records("train.csv"):
        y = rec["label"]
        lst = bucket.setdefault(y, [])
        if len(lst) < PER_CLASS:
            lst.append(featurize(rec))
    X = np.vstack([f for lst in bucket.values() for f in lst])
    labels = [y for y, lst in bucket.items() for _ in lst]
    model.fit(X, labels)

    Test accuracy drops sharply (observed gap ~0.69 on one skewed task) versus
    prior-preserving subsampling: frequent classes, which dominate the test
    rows, are predicted far less often than their true rate, while rare
    classes are over-predicted, so the per-row accuracy average falls.

    STRIDE = 25
    feats, labels = [], []
    for i, rec in enumerate(iter_records("train.csv")):
        if i % STRIDE != 0:
            continue
        feats.append(featurize(rec))
        labels.append(rec["label"])
    model.fit(np.vstack(feats), labels)

P1 — Constant confidence for AP-scored predictions

    confidence = 1.0
    pred_strings = []
    for sample_id in sub_df["Id"]:
        parts = []
        for cand in candidates:
            parts.append(f"{confidence} {cand['x']:.3f} {cand['y']:.3f} "
                         f"{cand['w']:.3f} {cand['h']:.3f} LABEL_A")
        pred_strings.append(" ".join(parts))
    sub_df["PredictionString"] = pred_strings
    sub_df.to_csv("submission.csv", index=False)

    All predictions tie at the same score, so the precision-recall sweep cannot
    place likely hits before unlikely ones; measured mAP is lower than the
    identical set of predictions ranked by empirical support (a full metric-unit
    gap was observed in a controlled contrastive run).

    max_n = max(c["n"] for c in candidates) if candidates else 1
    pred_strings = []
    for sample_id in sub_df["Id"]:
        parts = []
        for cand in candidates:
            conf = max(0.01, min(1.0, cand["n"] / max_n))
            parts.append(f"{conf} {cand['x']:.3f} {cand['y']:.3f} "
                         f"{cand['w']:.3f} {cand['h']:.3f} LABEL_A")
        pred_strings.append(" ".join(parts))
    sub_df["PredictionString"] = pred_strings

P1 — Unweighted probability blend without validated weights

    clf1.fit(X_tr, y)   # tree ensemble
    clf2.fit(X_tr, y)   # naive Bayes
    clf3.fit(X_tr, y)   # linear model
    p1 = clf1.predict_proba(X_te)
    p2 = clf2.predict_proba(X_te)
    p3 = clf3.predict_proba(X_te)
    blend = (p1 + p2 + p3) / 3
    pd.DataFrame(blend, columns=classes).to_csv("out.csv", index=False)

    Log loss on the evaluation set is substantially higher (observed +0.42)
    than a blend with weights optimized on out-of-fold predictions, because
    poorly calibrated members drag the average toward miscalibrated
    probabilities instead of being down-weighted.

    for name, m in models.items():
        oof[name], tst[name] = get_oof_preds(m, X_tr, y, X_te)
    best = min(
        ((log_loss(y, w*oof["a"] + (1-w)*oof["b"]), w)
         for w in np.arange(0, 1.01, 0.05))
    )
    _, w = best
    blend = w * tst["a"] + (1 - w) * tst["b"]
    pd.DataFrame(blend, columns=classes).to_csv("out.csv", index=False)

P1 — Per-class binary classifiers with post-hoc sum-normalization for a mutually exclusive multiclass target

    m1 = LogisticRegression().fit(X_tr, y_is_class1)
    m2 = LogisticRegression().fit(X_tr, y_is_class2)
    m3 = LogisticRegression().fit(X_tr, y_is_class3)
    p1 = m1.predict_proba(X_te)[:, 1]
    p2 = m2.predict_proba(X_te)[:, 1]
    p3 = m3.predict_proba(X_te)[:, 1]
    total = p1 + p2 + p3 + 1e-10
    sub = pd.DataFrame({'id': ids, 'LABEL_A': p1/total,
                        'LABEL_B': p2/total, 'LABEL_C': p3/total})
    sub.to_csv('submission.csv', index=False)

    Multiclass log loss is higher (worse) than a single softmax model on the
    same features — the independently fitted per-class probabilities are not
    jointly calibrated, and sum-normalization systematically distorts the
    predicted distribution (observed gap ≈ 0.04 log loss on the same data).

    y = np.argmax(df[['LABEL_A', 'LABEL_B', 'LABEL_C']].values, axis=1)
    model = LogisticRegression(multi_class='multinomial', max_iter=1000)
    model.fit(X_tr, y)
    preds = model.predict_proba(X_te)
    sub = pd.DataFrame({'id': ids, 'LABEL_A': preds[:, 0],
                        'LABEL_B': preds[:, 1], 'LABEL_C': preds[:, 2]})
    sub.to_csv('submission.csv', index=False)

P1 — Default-strength regularization for probabilistic linear classifier on high-dimensional handcrafted features

    from sklearn.linear_model import LogisticRegression
    from sklearn.preprocessing import StandardScaler

    scaler = StandardScaler()
    X_scaled = scaler.fit_transform(X)        # X: (n, 3000) handcrafted descriptors
    clf = LogisticRegression(max_iter=1000)   # default regularization strength
    clf.fit(X_scaled, y)                      # y has ~100 classes
    probs = clf.predict_proba(scaler.transform(X_test))
    pd.DataFrame(probs, columns=classes).to_csv("submission.csv", index=False)

    The run completes normally, but the near-interpolating boundary emits
    probabilities close to 0/1 on many test rows; wrong confident rows incur
    large per-row log-loss penalties, and the overall log loss is substantially
    higher (worse) than the same pipeline with a 100x stronger penalty, which
    scored ~0.62 better on the probability-quality metric.

    from sklearn.linear_model import LogisticRegression
    from sklearn.preprocessing import StandardScaler

    scaler = StandardScaler()
    X_scaled = scaler.fit_transform(X)
    clf = LogisticRegression(C=0.01, max_iter=1000)  # strong penalty
    clf.fit(X_scaled, y)
    probs = clf.predict_proba(scaler.transform(X_test))
    probs = 0.9 * probs + 0.1 / probs.shape[1]       # blend toward uniform
    pd.DataFrame(probs, columns=classes).to_csv("submission.csv", index=False)

P1 — Uniform probability averaging without validation-based weight selection

    clf1.fit(X_train, y_train)
    clf2.fit(X_train, y_train)
    clf3.fit(X_train, y_train)
    pred1 = clf1.predict_proba(X_test)
    pred2 = clf2.predict_proba(X_test)
    pred3 = clf3.predict_proba(X_test)
    ensemble_pred = (pred1 + pred2 + pred3) / 3
    pd.DataFrame(ensemble_pred).to_csv("submission.csv", index=False)

    Log loss on the evaluation set is substantially higher (worse) than a
    validation-tuned blend — observed gap of ~0.42 — because a poorly
    calibrated member receives the same 1/3 weight as well-calibrated
    members instead of being down-weighted toward zero.

    for name, model in models.items():
        oof[name], tst[name] = cross_val_probs(model, X_train, y, X_test)
    best = None
    for w1 in np.arange(0, 1.01, 0.05):
        for w2 in np.arange(0, 1.01 - w1 + 1e-9, 0.05):
            w3 = 1 - w1 - w2
            ll = log_loss(y, w1*oof["a"] + w2*oof["b"] + w3*oof["c"])
            if best is None or ll < best[0]:
                best = (ll, w1, w2, w3)
    _, w1, w2, w3 = best
    pred = w1*tst["a"] + w2*tst["b"] + w3*tst["c"]

P1 — Hardcoded uncertainty value in a metric that scores uncertainty

    preds = model.predict(X_test)
    sub = sample[["row_id"]].copy()
    sub["value"] = preds
    sub["uncertainty"] = 150.0  # fixed guess
    sub["value"] = sub["value"].fillna(train["value"].median())
    sub["uncertainty"] = sub["uncertainty"].fillna(150.0)
    sub.to_csv("submission.csv", index=False)

    The likelihood-style score is systematically lower (observed ~3.7% relative
    degradation) because the constant uncertainty is mis-sized relative to the
    actual residual distribution the metric penalizes: too small inflates the
    error term, too large inflates the log-penalty term.

    best = (1e9, None, None)
    for c0 in range(60, 301, 10):
        for c1 in np.arange(0.0, 6.0, 0.5):
            sigma = np.clip(c0 + c1 * np.abs(dw_val), 70, None)
            err = np.clip(np.abs(y_val - pred_val), 0, 1000)
            score = -np.mean(-np.sqrt(2) * err / sigma - np.log(np.sqrt(2) * sigma))
            if score < best[0]:
                best = (score, c0, c1)
    _, C0, C1 = best
    sub["uncertainty"] = np.clip(C0 + C1 * np.abs(sub["dw"]), 70, None)

P1 — Class rebalancing before probability-scored output without recalibration

    model = LogisticRegression(class_weight='balanced', max_iter=1000)
    model.fit(X_train, y_train)
    probs = model.predict_proba(X_test)
    submission = pd.DataFrame(probs, columns=['LABEL_A', 'LABEL_B', 'LABEL_C'])
    submission.insert(0, 'id', test_df['id'])
    submission.to_csv('submission.csv', index=False)

    Log loss increases (e.g., +0.02 or more versus an unweighted fit) because
    every row's predicted probabilities are systematically inflated for
    minority classes and deflated for the majority class, deviating from the
    true class priors that the proper scoring rule rewards.

    base = LogisticRegression(class_weight='balanced', max_iter=1000)
    model = CalibratedClassifierCV(base, method='isotonic', cv=3)
    model.fit(X_train, y_train)
    probs = model.predict_proba(X_test)
    submission = pd.DataFrame(probs, columns=['LABEL_A', 'LABEL_B', 'LABEL_C'])
    submission.insert(0, 'id', test_df['id'])
    submission.to_csv('submission.csv', index=False)

P1 — Hardcoded uncertainty value where the metric scores calibration

    model = LinearRegression().fit(X_train, y_train)
    submission["value"] = model.predict(X_test)
    # confidence: fixed floor plus distance-from-baseline heuristic
    submission["confidence"] = 150.0 + 2.0 * submission["weeks_from_base"].abs()
    submission["confidence"] = submission["confidence"].fillna(150.0)
    submission.to_csv("submission.csv", index=False)

    The likelihood-based score is systematically worse (~2.9% relative in the
    measured pair) than the score achievable with the same point predictions,
    because the emitted uncertainty is miscalibrated versus the residual scale
    that the metric formula rewards.

    resid = y_train - model.predict(X_train)
    def metric_loss(sigma):
        s = np.maximum(sigma, 70.0)
        d = np.minimum(np.abs(resid), 1000.0)
        return np.mean(np.sqrt(2) * d / s + np.log(np.sqrt(2) * s))
    best_sigma = minimize(metric_loss, x0=[200.0], method="Nelder-Mead").x[0]
    submission["value"] = model.predict(X_test)
    submission["confidence"] = best_sigma
    submission.to_csv("submission.csv", index=False)

P1 — Constant global uncertainty ignoring error-driving covariate

    resid = train["y"] - train_pred
    best_sigma = float(np.std(resid))

    sub["y_pred"] = predict(sub_features)
    sub["uncertainty"] = best_sigma
    sub[["row_id", "y_pred", "uncertainty"]].to_csv("submission.csv", index=False)

    Averaged log-likelihood score drops relative to horizon-scaled uncertainty:
    near-baseline rows (small true error) are over-penalized by an inflated sigma,
    while far-horizon rows (large true error) incur heavy miss penalties from an
    underestimated sigma. Observed gap on the same predictions: ~0.028 metric points.

    resid = train["y"] - train_pred
    a, b = fit_sigma_params(resid, train["dw"])

    sub["y_pred"] = predict(sub_features)
    sub["uncertainty"] = np.maximum(a + b * np.abs(sub["dw"].values), 70.0)
    sub[["row_id", "y_pred", "uncertainty"]].to_csv("submission.csv", index=False)

P1 — Raw probabilities submitted without clipping or prior-blending under log loss

    probs = model.predict_proba(X_test_scaled)
    prob_df = pd.DataFrame(probs, columns=classes)
    prob_df = prob_df.reindex(columns=submission_cols, fill_value=0.0)
    row_sums = prob_df.sum(axis=1).replace(0, 1)
    prob_df = prob_df.div(row_sums, axis=0)
    submission = pd.concat([pd.DataFrame({"id": test_ids}), prob_df], axis=1)
    submission.to_csv("submission.csv", index=False)

    Log loss inflates far above the uniform baseline (e.g. 11.9 vs ln(K)=4.79),
    because rows where the model assigns near-zero probability to the true
    class contribute penalties approaching -log(0); a clipped/blended variant
    of the same pipeline scores ~0.6 better on the competition metric.

    probs = model.predict_proba(X_test_scaled)
    out = pd.DataFrame(0.0, index=range(len(test_ids)), columns=classes)
    for j, c in enumerate(model.classes_):
        out[c] = probs[:, j]
    K = len(classes)
    out = 0.9 * out.values + 0.1 * (1.0 / K)
    sub = pd.DataFrame(out, columns=classes)
    sub.insert(0, "id", test_ids)
    sub.to_csv("submission.csv", index=False)

P1 — Unvalidated equal-weight probability blend

    clf1.fit(X_train, y_train)
    clf2.fit(X_train, y_train)
    clf3.fit(X_train, y_train)

    p1 = clf1.predict_proba(X_test)
    p2 = clf2.predict_proba(X_test)
    p3 = clf3.predict_proba(X_test)

    blend = (p1 + p2 + p3) / 3
    sub = pd.DataFrame(blend, columns=classes)
    sub.to_csv("submission.csv", index=False)

    The scored log loss is substantially worse (observed ~2x higher, a 0.50
    absolute gap) than a validated blend, because a miscalibrated component
    emitting near-0/1 probabilities drags the uniform average toward
    confident wrong predictions with no mechanism to detect or down-weight it.

    oof = [cross_val_predict(m, X_train, y_train, cv=5,
                             method="predict_proba") for m in models]
    scores = [log_loss(y_train, o) for o in oof]
    keep = [m for m, s in zip(models, scores) if s < 1.5 * min(scores)]
    for m in keep:
        m.fit(X_train, y_train)
    preds = [m.predict_proba(X_test) for m in keep]
    blend = np.mean(preds, axis=0)
    sub = pd.DataFrame(blend, columns=classes)
    sub.to_csv("submission.csv", index=False)

P1 — Hardcoded uncertainty constants never tuned against the metric

    c0, c1 = 200.0, 3.0
    for i, (p, w) in enumerate(zip(patients, weeks)):
        dw = w - base_week[p]
        fvc[i] = base_val[p] + slope[p] * dw
        conf[i] = c0 + c1 * abs(dw)
    out = pd.DataFrame({"Patient_Week": ids,
                        "FVC": fvc,
                        "Confidence": conf})
    out.to_csv("submission.csv", index=False)

    The uncertainty-aware metric (Gaussian-NLL-style, higher is better) lands
    roughly 5% relative below a solution that tunes the confidence intercept
    and distance slope against the metric on out-of-fold residuals; the
    submitted confidences are systematically mis-calibrated in one direction.

    best = (-1e9, None)
    for c0 in range(100, 401, 25):
        for c1 in [0.0, 1.0, 2.0, 4.0]:
            sig = np.clip(c0 + c1 * np.abs(oof_dw), 70, 1000)
            d = np.minimum(np.abs(oof_resid), 1000)
            score = np.mean(-np.sqrt(2) * d / sig - np.log(np.sqrt(2) * sig))
            if score > best[0]:
                best = (score, (c0, c1))
    c0, c1 = best[1]
    conf = np.clip(c0 + c1 * np.abs(test_dw), 70, 1000)
    out = pd.DataFrame({"Patient_Week": ids, "FVC": fvc, "Confidence": conf})

P1 — Validation scores computed but ignored in ensemble combination

    models = {"a": model_a, "b": model_b}
    preds = []
    for name, m in models.items():
        sc = cross_val_score(m, X_tr, y_tr, cv=cv, scoring="roc_auc")
        print(name, "cv", sc.mean())
        m.fit(X_tr, y_tr)
        preds.append(m.predict_proba(X_te)[:, 1])
    p = np.mean(preds, axis=0)
    sub["LABEL_A"] = p
    sub.to_csv("submission.csv", index=False)

    A member whose validation AUC is markedly worse than the best member
    contributes with the same weight, dragging the blended test AUC below
    what the best member or a score-weighted blend achieves (observed gap
    on the order of 0.08 in the ranking metric).

    scores, preds = [], []
    for name, m in models.items():
        sc = cross_val_score(m, X_tr, y_tr, cv=cv, scoring="roc_auc").mean()
        scores.append(sc)
        m.fit(X_tr, y_tr)
        preds.append(m.predict_proba(X_te)[:, 1])
    w = np.array(scores) - 0.5
    w = np.clip(w, 0, None) / np.clip(w, 0, None).sum()
    p = np.average(preds, axis=0, weights=w)

P1 — Uncalibrated probabilities written to log-loss submission

    clf.fit(X_train, y_train)
    probs = clf.predict_proba(X_test)
    prob_df = pd.DataFrame(probs, columns=classes)
    row_sums = prob_df.sum(axis=1).replace(0, 1)
    prob_df = prob_df.div(row_sums, axis=0)
    sub = pd.concat([pd.DataFrame({"id": test_ids}), prob_df], axis=1)
    sub.to_csv("submission.csv", index=False)

    Log loss inflated to 11.63 vs 4.55 for the same pipeline with a
    uniform blend; overconfident wrong rows contribute near-unbounded
    -log(p) terms, pushing the score below the ~4.79 uniform baseline.

    clf.fit(X_train, y_train)
    probs = clf.predict_proba(X_test)
    K = probs.shape[1]
    probs = 0.9 * probs + 0.1 / K
    prob_df = pd.DataFrame(probs, columns=classes)
    sub = pd.concat([pd.DataFrame({"id": test_ids}), prob_df], axis=1)
    sub.to_csv("submission.csv", index=False)

P1 — Empty prediction strings for all detection instances

    predictions = []
    for sample_id in sample_submission['Id'].tolist():
        # avoid false positives which penalize the score
        pred_string = ""
        predictions.append({'Id': sample_id,
                            'PredictionString': pred_string})
    pd.DataFrame(predictions).to_csv(OUTPUT_PATH, index=False)

    Recall is 0 for every instance, so the precision/recall-based metric is
    pinned at 0.0 regardless of upstream computation; emitting even a fixed
    prior detection per instance yields a strictly positive score.

    strings = []
    for tok in sub["Id"].values:
        pose = poses.get(tok)
        if pose is None:
            strings.append("")
            continue
        strings.append(format_prior_boxes(pose, priors))
    sub["PredictionString"] = strings
    sub.to_csv(OUT, index=False)

P1 — Single default-configured fit with no validation-guided selection for a probability metric

    vec = TfidfVectorizer(ngram_range=(1, 2))
    Xtr = vec.fit_transform(train_df["text_col"])
    Xte = vec.transform(test_df["text_col"])
    model = LogisticRegression(max_iter=1000, random_state=42)
    model.fit(Xtr, train_df["target"])
    proba = model.predict_proba(Xte)
    sub = pd.DataFrame(proba, columns=model.classes_)
    sub.insert(0, "id", test_df["id"])
    sub.to_csv("submission.csv", index=False)

    Log loss on the leaderboard is ~0.58 versus ~0.38 for the same features
    with cross-validated hyperparameter and blend selection: a ~0.20 worse
    score from submitting unoptimized default-regularization probabilities.

    best = None
    for C in [0.1, 0.5, 1.0, 4.0]:
        skf = StratifiedKFold(5, shuffle=True, random_state=42)
        oof = np.zeros((len(y), 3))
        for tr, va in skf.split(Xtr, y):
            m = LogisticRegression(C=C, max_iter=1000).fit(Xtr[tr], y[tr])
            oof[va] = m.predict_proba(Xtr[va])
        s = log_loss(y, oof)
        if best is None or s < best[0]:
            best = (s, C)
    final = LogisticRegression(C=best[1], max_iter=1000).fit(Xtr, y)
    sub = pd.DataFrame(final.predict_proba(Xte), columns=final.classes_)

P1 — Hardcoded uncertainty constants never tuned against the scoring formula

    fvc = np.zeros(len(sub))
    conf = np.zeros(len(sub))
    for i, (p, w) in enumerate(zip(patients, weeks)):
        r = base.loc[p]
        dw = w - r["base_week"]
        fvc[i] = r["base_val"] + r["pred_slope"] * dw
        conf[i] = 200.0 + 5.0 * abs(dw)   # fixed heuristic constants
    out = pd.DataFrame({"id": sub["id"], "pred": fvc, "sigma": conf})
    out.to_csv("submission.csv", index=False)

    Program completes and produces a valid submission, but the likelihood-style
    metric is systematically worse (~5% relative) than the same pipeline with
    the two uncertainty constants grid-searched against the exact scoring
    formula on held-out errors, because the fixed sigma is miscalibrated to
    the actual error distribution.

    best = (None, -1e18)
    for c0 in np.arange(100, 400, 10):
        for c1 in np.arange(0, 10, 0.5):
            sig = np.clip(c0 + c1 * np.abs(oof_dw), 70, None)
            d = np.minimum(np.abs(oof_err), 1000)
            score = np.mean(-np.sqrt(2)*d/sig - np.log(np.sqrt(2)*sig))
            if score > best[1]:
                best = ((c0, c1), score)
    (c0, c1) = best[0]
    conf = c0 + c1 * np.abs(dw)

P1 — No held-out estimate of the scored metric before writing predictions

    import pandas as pd
    from sklearn.linear_model import LogisticRegression

    train = pd.read_csv("train.csv")
    test = pd.read_csv("test.csv")
    X, y = train[["col_a", "col_b"]], train["target"]

    model = LogisticRegression(C=1.0, max_iter=1000)
    model.fit(X, y)
    preds = model.predict_proba(test[["col_a", "col_b"]])
    pd.DataFrame(preds).to_csv("submission.csv", index=False)

    The regularization strength, feature set, and model family are never
    checked against the scored metric; the submitted log loss lands
    substantially higher (e.g. +0.4) than a configuration selected via
    out-of-fold measurement on the same data.

    import pandas as pd
    from sklearn.linear_model import LogisticRegression
    from sklearn.model_selection import cross_val_predict
    from sklearn.metrics import log_loss

    train = pd.read_csv("train.csv")
    test = pd.read_csv("test.csv")
    X, y = train[["col_a", "col_b"]], train["target"]

    model = LogisticRegression(C=1.0, max_iter=1000)
    oof = cross_val_predict(model, X, y, method="predict_proba", cv=5)
    print("cv log loss:", log_loss(y, oof))
    model.fit(X, y)
    pd.DataFrame(model.predict_proba(test[["col_a", "col_b"]])).to_csv("submission.csv", index=False)

P1 — Per-class binary decomposition with prior-distorting reweighting under a probability-scored multiclass metric

    m_a = LogisticRegression(class_weight='balanced')
    m_b = LogisticRegression(class_weight='balanced')
    m_c = LogisticRegression(class_weight='balanced')
    m_a.fit(X_tr, y_a); m_b.fit(X_tr, y_b); m_c.fit(X_tr, y_c)
    p_a = m_a.predict_proba(X_te)[:, 1]
    p_b = m_b.predict_proba(X_te)[:, 1]
    p_c = m_c.predict_proba(X_te)[:, 1]
    tot = p_a + p_b + p_c + 1e-10
    out = pd.DataFrame({'LABEL_A': p_a/tot, 'LABEL_B': p_b/tot,
                        'LABEL_C': p_c/tot})

    Predicted probability vectors are pushed toward uniform class priors by the
    balanced reweighting, then distorted further by post-hoc renormalization;
    log loss on held-out data is roughly 0.04 worse than a single multiclass
    model fitted on identical features.

    y = df['label'].map({'LABEL_A': 0, 'LABEL_B': 1, 'LABEL_C': 2})
    model = LogisticRegression(multi_class='multinomial', max_iter=1000)
    model.fit(X_tr, y)
    probs = model.predict_proba(X_te)
    out = pd.DataFrame({'LABEL_A': probs[:, 0], 'LABEL_B': probs[:, 1],
                        'LABEL_C': probs[:, 2]})

P1 — Class rebalancing under a proper scoring rule

    from sklearn.linear_model import LogisticRegression
    model = LogisticRegression(class_weight='balanced', max_iter=1000)
    model.fit(X_train, y_train)
    probs = model.predict_proba(X_test)
    submission = pd.DataFrame(probs, columns=['LABEL_A', 'LABEL_B', 'LABEL_C'])
    submission.insert(0, 'id', test_df['id'])
    submission.to_csv('submission.csv', index=False)

    Predicted probabilities are pulled toward a uniform class distribution
    rather than the true skewed priors; minority classes are systematically
    over-weighted, and log loss on the evaluation set rises (~0.03-0.04 worse)
    compared with an unweighted fit on identical features.

    from sklearn.linear_model import LogisticRegression
    model = LogisticRegression(max_iter=1000)
    model.fit(X_train, y_train)
    probs = model.predict_proba(X_test)
    submission = pd.DataFrame(probs, columns=['LABEL_A', 'LABEL_B', 'LABEL_C'])
    submission.insert(0, 'id', test_df['id'])
    submission.to_csv('submission.csv', index=False)

P1 — Blind probability submission without held-out metric evaluation

    vec = TfidfVectorizer(max_features=20000)
    X = vec.fit_transform(train["text"])
    Xt = vec.transform(test["text"])
    model = LogisticRegression(max_iter=1000)
    model.fit(X, train["label"])
    proba = model.predict_proba(Xt)
    sub = pd.DataFrame(proba, columns=classes)
    sub.insert(0, "id", test["id"])
    sub.to_csv("submission.csv", index=False)

    Model choice, regularization strength, and calibration are never checked
    against the scored loss; submitted log loss is systematically higher
    (observed gap: 0.335 worse) than metric-guided selection over the same
    model family would achieve.

    skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=0)
    oof = np.zeros((len(train), n_classes))
    test_pred = np.zeros((len(test), n_classes))
    for tr, va in skf.split(X, y):
        m = LogisticRegression(max_iter=1000).fit(X[tr], y[tr])
        oof[va] = m.predict_proba(X[va])
        test_pred += m.predict_proba(Xt) / skf.n_splits
    print("oof log loss:", log_loss(y, oof))
    pd.DataFrame(test_pred, columns=classes).to_csv("submission.csv", index=False)

P1 — Unclipped probabilities under an unbounded log-based metric

    probs = clf.predict_proba(X_test)
    prob_df = pd.DataFrame(probs, columns=classes)
    prob_df = prob_df[submission_cols]
    row_sums = prob_df.sum(axis=1)
    prob_df = prob_df.div(row_sums, axis=0)
    sub = pd.concat([pd.DataFrame({"id": test_ids}), prob_df], axis=1)
    sub.to_csv("submission.csv", index=False)

    Rows where the model assigns near-zero probability to the true class each
    contribute -log(p) ≈ 15-35 to the mean log loss; overall score degrades
    from ~4.5 (with smoothing) to ~11.9, worse than a uniform-prediction
    baseline of ~4.8.

    probs = clf.predict_proba(X_test)
    K = probs.shape[1]
    probs = 0.9 * probs + 0.1 / K
    prob_df = pd.DataFrame(probs, columns=classes)
    sub = pd.concat([pd.DataFrame({"id": test_ids}), prob_df], axis=1)
    sub.to_csv("submission.csv", index=False)

P1 — Arithmetic mean of wrap-around angles

    import numpy as np

    def summarize_group(rows):
        yaws = np.array([r["yaw"] for r in rows])  # radians in [-pi, pi]
        return {
            "cx": np.mean([r["x"] for r in rows]),
            "cy": np.mean([r["y"] for r in rows]),
            "yaw": np.mean(yaws),
        }

    For groups whose yaws straddle the ±π wrap (e.g. 3.1 and -3.1 rad), the
    aggregated yaw comes out near 0 rad — roughly 180° off the true heading —
    so oriented-box overlap with ground truth drops sharply and the
    overlap-based evaluation score is systematically lower than with a
    circular mean.

    import numpy as np

    def summarize_group(rows):
        yaws = np.array([r["yaw"] for r in rows])
        mean_yaw = np.arctan2(np.mean(np.sin(yaws)),
                              np.mean(np.cos(yaws)))
        return {
            "cx": np.mean([r["x"] for r in rows]),
            "cy": np.mean([r["y"] for r in rows]),
            "yaw": mean_yaw,
        }

P1 — Empty prediction strings submitted under an average-precision metric

    predictions = []
    for sample_id in sample_submission['Id'].tolist():
        if sample_id in test_token_to_sample:
            # Empty predictions to avoid false positives penalizing the score
            pred_string = ""
        else:
            pred_string = ""
        predictions.append({'Id': sample_id, 'PredictionString': pred_string})
    pd.DataFrame(predictions).to_csv(OUTPUT_PATH, index=False)

    Recall is 0 for every class, so average precision is 0.0 for all classes and
    the final mAP score is pinned at the metric's minimum — strictly below any
    run that emits even weak candidate detections, which scored 1.0 higher on
    the same evaluation.

    predictions = []
    for sample_id in sample_submission['Id'].tolist():
        boxes = predict_boxes(sample_id)  # list of (conf, x, y, z, w, l, h, yaw, cls)
        if boxes:
            pred_string = " ".join(format_box(b) for b in boxes)
        else:
            pred_string = ""  # only when the model truly found nothing here
        predictions.append({'Id': sample_id, 'PredictionString': pred_string})
    pd.DataFrame(predictions).to_csv(OUTPUT_PATH, index=False)

P1 — Constant confidence for ranked-detection scoring

    pred_tokens = []
    for cand in candidates:
        x, y, z = cand["x"], cand["y"], cand["z"]
        conf = 1.0
        pred_tokens.append(f"{conf} {x:.3f} {y:.3f} {z:.3f} LABEL_A")
    results.append({"Id": sample_id, "PredictionString": " ".join(pred_tokens)})
    pd.DataFrame(results).to_csv("submission.csv", index=False)

    Average precision drops sharply (observed ~8x lower) because weak
    candidates tie with strong ones at identical rank, interleaving false
    positives among true positives in the precision-recall sweep.

    pred_tokens = []
    max_n = max(c["n"] for c in candidates)
    for cand in candidates:
        x, y, z = cand["x"], cand["y"], cand["z"]
        conf = max(0.01, min(1.0, cand["n"] / max_n))
        pred_tokens.append(f"{conf} {x:.3f} {y:.3f} {z:.3f} LABEL_A")
    results.append({"Id": sample_id, "PredictionString": " ".join(pred_tokens)})
    pd.DataFrame(results).to_csv("submission.csv", index=False)

P1 — Hard-coded uncertainty value in likelihood-scored submission

    preds = model.predict(X_test)
    submission = pd.DataFrame({
        "id": test_ids,
        "value": preds,
        "Confidence": 150.0,   # fixed guess
    })
    submission["Confidence"] = submission["Confidence"].fillna(150.0)
    submission.to_csv("submission.csv", index=False)

    The likelihood-based score is ~3% worse than the same predictions
    submitted with an uncertainty tuned on training residuals, because the
    fixed sigma is miscalibrated relative to the score-maximizing value.

    resid = train["value"] - model.predict(X_train)
    def neg_metric(sigma):
        s = np.maximum(sigma, 70)
        d = np.minimum(np.abs(resid), 1000)
        return np.mean(np.sqrt(2) * d / s + np.log(np.sqrt(2) * s))
    best_sigma = minimize(neg_metric, x0=[200.0], method="Nelder-Mead").x[0]
    submission["Confidence"] = best_sigma
    submission.to_csv("submission.csv", index=False)

P1 — Single hardcoded prediction per multi-object sample

    preds = []
    for s in test_samples:
        feats = extract_features(s)              # one vector per scene
        x = model_x.predict([feats])[0]
        y = model_y.predict([feats])[0]
        z = model_z.predict([feats])[0]
        pred = f"1.0 {x:.3f} {y:.3f} {z:.3f} 1.9 4.5 1.6 0.0 LABEL_A"
        preds.append({"Id": s["token"], "PredictionString": pred})
    pd.DataFrame(preds).to_csv("submission.csv", index=False)

    Recall is capped at one object per scene even when scenes contain dozens
    of objects, and every class other than the hardcoded one scores exactly
    zero AP; the mAP-style metric drops to near or exactly 0 versus a
    baseline that emits multiple per-class predictions per scene.

    preds = []
    for s in test_samples:
        tokens = []
        for cls, stats in class_stats.items():          # every class
            for _ in range(stats["avg_count"]):         # variable count
                x, y, z = sample_position(s, stats)
                tokens.append(f"1.0 {x:.3f} {y:.3f} {z:.3f} "
                              f"{stats['w']} {stats['l']} {stats['h']} 0.0 {cls}")
        preds.append({"Id": s["token"], "PredictionString": " ".join(tokens)})
    pd.DataFrame(preds).to_csv("submission.csv", index=False)

P1 — Empty prediction strings under a recall-bounded metric

    predictions = []
    for sample_id in test_ids:
        # Emit nothing to avoid false positives, which are penalized
        pred_string = ""
        predictions.append({"Id": sample_id, "PredictionString": pred_string})

    pd.DataFrame(predictions).to_csv(OUTPUT_PATH, index=False)

    Average-precision score is exactly 0 because recall is 0 for every sample;
    even naive prior-based guesses (fixed-size boxes at expected locations)
    score strictly higher, so the submission underperforms any trivial baseline.

    predictions = []
    for sample_id in test_ids:
        boxes = model_boxes.get(sample_id, [])
        if not boxes:
            pred_string = ""  # only when the model truly found nothing
        else:
            pred_string = " ".join(fmt(b) for b in boxes)
        predictions.append({"Id": sample_id, "PredictionString": pred_string})
    pd.DataFrame(predictions).to_csv(OUTPUT_PATH, index=False)

P1 — Uniform blend includes uncalibrated bagged-tree probabilities under a proper scoring rule

    vec = TfidfVectorizer(max_features=50000)
    X_tr = vec.fit_transform(train_text); X_te = vec.transform(test_text)
    clf1 = RandomForestClassifier(n_estimators=100)
    clf2 = MultinomialNB(alpha=0.1)
    clf3 = LogisticRegression(C=1.0)
    for c in (clf1, clf2, clf3):
        c.fit(X_tr, y)
    p1 = clf1.predict_proba(X_te)
    p2 = clf2.predict_proba(X_te)
    p3 = clf3.predict_proba(X_te)
    ensemble_pred = (p1 + p2 + p3) / 3
    pd.DataFrame(ensemble_pred, columns=classes).to_csv("submission.csv")

    Log loss on the held-out evaluation increases (worsens) by roughly 0.4
    relative to a validation-weighted blend of the calibrated members only,
    because the forest's vote-fraction probabilities are miscalibrated on
    sparse text features and pull every prediction toward those values with
    full equal weight.

    vec = TfidfVectorizer(max_features=50000)
    X_tr = vec.fit_transform(train_text); X_te = vec.transform(test_text)
    rf = CalibratedClassifierCV(RandomForestClassifier(n_estimators=100), cv=3)
    lr = LogisticRegression(C=1.0)
    rf.fit(X_tr, y); lr.fit(X_tr, y)
    best_w = min(weight_grid, key=lambda w: log_loss(
        y_val, w * rf.predict_proba(X_val) + (1 - w) * lr.predict_proba(X_val)))
    final = best_w * rf.predict_proba(X_te) + (1 - best_w) * lr.predict_proba(X_te)
    pd.DataFrame(final, columns=classes).to_csv("submission.csv")

P1 — Constant uncertainty under a calibration-scored metric

    from scipy.optimize import minimize

    res = minimize(neg_metric, x0=[200.0], args=(y_val, pred_val))
    best_sigma = res.x[0]

    sub_df["value"] = model.predict(X_sub)
    sub_df["conf"] = best_sigma          # same constant for every row
    sub_df["conf"] = sub_df["conf"].round(1)
    sub_df[["row_id", "value", "conf"]].to_csv("submission.csv", index=False)

    Average likelihood-based score drops (~0.03 worse than a horizon-scaled
    confidence on the same predictions): easy near-baseline rows are penalized
    by inflated sigma, and far-horizon rows are penalized by clipped errors
    exceeding the understated sigma.

    dw = sub_df["target_week"] - sub_df["base_week"]
    conf = a + b * np.abs(dw.values.astype(float))
    conf = np.maximum(conf, 70.0)        # scalar floor, not a constant value
    sub_df["conf"] = conf
    sub_df[["row_id", "value", "conf"]].to_csv("submission.csv", index=False)

P1 — Hardcoded uncertainty channel under a likelihood-based metric

    delta = model.predict(Xte)
    fvc = pred_df["base_val"].values + delta
    a, b = 200.0, 3.0
    conf = a + b * np.abs(pred_df["dw"].values.astype(float))
    conf = np.maximum(conf, 70.0)
    out = pd.DataFrame({
        "row_id": pred_df["row_id"],
        "value": fvc,
        "Confidence": conf,
    })
    out.to_csv("submission.csv", index=False)

    The predicted sigma is systematically miscalibrated relative to the
    likelihood metric; final leaderboard score is several percent worse
    (observed 5.6% relative) than the same point predictions paired with
    a sigma tuned on out-of-fold errors.

    oof_err = np.abs(y_oof - oof_pred)
    dw = np.abs(oof_dw)
    best, best_ll = None, -np.inf
    for a in np.linspace(50, 400, 36):
        for b in np.linspace(0, 10, 21):
            s = np.maximum(a + b * dw, 70.0)
            ll = np.mean(-np.sqrt(2) * oof_err / s - np.log(np.sqrt(2) * s))
            if ll > best_ll:
                best_ll, best = ll, (a, b)
    a, b = best
    conf = np.maximum(a + b * np.abs(test_dw), 70.0)

P1 — Universally empty prediction field in a ranked-detection submission

    predictions = []
    for sample_id in sample_submission['Id'].tolist():
        if sample_id in test_index:
            # Empty output avoids false positives, which are penalized
            pred_string = ""
        else:
            pred_string = ""
        predictions.append({'Id': sample_id, 'PredictionString': pred_string})
    pd.DataFrame(predictions).to_csv(OUTPUT_PATH, index=False)

    Recall is 0 for every instance, so the average-precision-style score is
    exactly 0.0; any submission containing even one low-confidence prior-based
    candidate per instance scores strictly higher (e.g., 0.0 -> 0.00057),
    because AP ranks predictions by confidence and cannot fall below empty.

    strings = []
    for tok in sub["Id"].values:
        pose = test_meta.get(tok)
        if pose is None or not cands:
            strings.append("")
            continue
        out = ["%.3f %.3f %.3f %.3f" % (c["conf"], c["x"], c["y"], c["z"])
               for c in cands]
        strings.append(" ".join(out))
    sub["PredictionString"] = strings
    sub.to_csv(OUT, index=False)

P1 — Single fixed-confidence detection per sample under an average-precision metric

    predictions = []
    for sample in test_samples:
        x, y, z = estimate_center(sample)
        w, l, h, yaw = 1.9, 4.5, 1.7, 0.0
        pred_string = f"1.0 {x:.3f} {y:.3f} {z:.3f} {w} {l} {h} {yaw} OBJ_A"
        predictions.append({'Id': sample['token'],
                            'PredictionString': pred_string})
    pd.DataFrame(predictions).to_csv('submission.csv', index=False)

    Recall is capped at one object per sample while scenes contain many;
    the constant confidence provides no ranking signal for the AP sweep,
    so mAP collapses toward zero relative to multi-candidate outputs
    (observed metric gap of 1.0 versus a graded multi-candidate baseline).

    strings = []
    for sample in test_samples:
        out = []
        for cd in candidates_for(sample):
            conf = max(0.01, min(1.0, cd['support'] / max_support))
            out.append(f"{conf:.4f} {cd['x']:.3f} {cd['y']:.3f} {cd['z']:.3f} "
                       f"{cd['w']} {cd['l']} {cd['h']} {cd['yaw']} OBJ_A")
        strings.append(" ".join(out))
    sub['PredictionString'] = strings
    sub.to_csv('submission.csv', index=False)

P1 — Unvalidated equal-weight blend including tree-ensemble probabilities on sparse text under a probabilistic metric

    vec = TfidfVectorizer()
    X_tr = vec.fit_transform(train['col_a']); X_te = vec.transform(test['col_a'])
    clf1 = LogisticRegression().fit(X_tr, y)
    clf2 = MultinomialNB().fit(X_tr, y)
    clf3 = RandomForestClassifier(n_estimators=100).fit(X_tr, y)
    p1 = clf1.predict_proba(X_te)
    p2 = clf2.predict_proba(X_te)
    p3 = clf3.predict_proba(X_te)
    final = (p1 + p2 + p3) / 3
    pd.DataFrame(final).to_csv('submission.csv', index=False)

    Log loss on the scored set rises substantially (observed +0.42 versus a
    validated blend of well-calibrated models on the same features), because
    the tree ensemble's overconfident/flat probabilities on sparse text pull
    the averaged estimates away from calibrated values.

    vec = TfidfVectorizer()
    X_tr = vec.fit_transform(train['col_a']); X_te = vec.transform(test['col_a'])
    oof_lr, oof_nb = np.zeros((len(y), 3)), np.zeros((len(y), 3))
    for tr_idx, va_idx in StratifiedKFold(5).split(X_tr, y):
        lr = LogisticRegression().fit(X_tr[tr_idx], y[tr_idx])
        nb = MultinomialNB().fit(X_tr[tr_idx], y[tr_idx])
        oof_lr[va_idx] = lr.predict_proba(X_tr[va_idx])
        oof_nb[va_idx] = nb.predict_proba(X_tr[va_idx])
    best_w = min(np.arange(0, 1.05, 0.05),
                 key=lambda w: log_loss(y, w*oof_lr + (1-w)*oof_nb))
    final = best_w * lr.predict_proba(X_te) + (1-best_w) * nb.predict_proba(X_te)

P1 — Constant uncertainty ignoring forecast horizon

    sub = sample_sub.merge(base, on="entity", how="left")
    sub["dt"] = sub["t"] - sub["t_base"]
    sub["value"] = predict_value(sub)
    best_sigma = 230.0
    sub["Confidence"] = best_sigma
    sub[["row_id", "value", "Confidence"]].to_csv("submission.csv", index=False)

    Rows far from the baseline time have larger true errors but the same sigma,
    so their error/sigma penalty inflates; the averaged likelihood score drops
    (observed gap ~0.025 on the metric versus a horizon-scaled sigma).

    sub = sample_sub.merge(base, on="entity", how="left")
    sub["dt"] = sub["t"] - sub["t_base"]
    sub["value"] = predict_value(sub)
    c0, c1 = 180.0, 4.5
    sub["Confidence"] = c0 + c1 * sub["dt"].abs()
    sub[["row_id", "value", "Confidence"]].to_csv("submission.csv", index=False)

P1 — Default regularization for probabilistic linear classifier on high-dimensional weak features

    from sklearn.linear_model import LogisticRegression
    from sklearn.preprocessing import StandardScaler

    # X: (n_samples, 8100) descriptor features, y: many-class labels
    scaler = StandardScaler()
    Xs = scaler.fit_transform(X)
    clf = LogisticRegression(max_iter=1000)
    clf.fit(Xs, y)
    probs = clf.predict_proba(scaler.transform(X_test))
    pd.DataFrame(probs, columns=classes).to_csv("submission.csv", index=False)

    Under-regularized weights drive predicted probabilities toward 0/1;
    every misclassified row incurs a near-maximal per-row log loss, and the
    submitted log loss is more than 2x worse than a strongly regularized fit
    of the same features (observed gap ~0.61 on the probabilistic metric).

    from sklearn.linear_model import LogisticRegression
    from sklearn.metrics import log_loss

    best_C, best_ll = None, float("inf")
    for C in [0.001, 0.01, 0.1, 1.0]:
        m = LogisticRegression(C=C, max_iter=1000).fit(X_tr, y_tr)
        ll = log_loss(y_va, m.predict_proba(X_va))
        if ll < best_ll:
            best_C, best_ll = C, ll
    clf = LogisticRegression(C=best_C, max_iter=1000).fit(X, y)
    probs = clf.predict_proba(X_test)

P1 — Constant confidence for all emitted detections under a ranking-sensitive metric

    confidence = 1.0
    rows = []
    for sample_id in sub_df["Id"]:
        parts = []
        for dx, dy in offsets:
            wx, wy = transform(dx, dy)
            parts.append(f"{confidence} {wx:.3f} {wy:.3f} {wz:.3f} "
                         f"{w:.3f} {l:.3f} {h:.3f} {yaw:.4f} LABEL_A")
        rows.append(" ".join(parts))
    sub_df["PredictionString"] = rows
    sub_df.to_csv("submission.csv", index=False)

    Average precision drops relative to score-ordered output: because every
    prediction shares the same confidence, low-quality candidates are ranked
    tied with the best ones, so false positives appear early in the
    precision-recall sweep and depress precision at every recall level.

    rows = []
    for sample_id in sub_df["Id"]:
        parts = []
        for cand in candidates:
            conf = max(0.01, min(1.0, cand["support"] / max_support))
            wx, wy = transform(cand["dx"], cand["dy"])
            parts.append(f"{conf} {wx:.3f} {wy:.3f} {wz:.3f} "
                          f"{cand['w']:.3f} {cand['l']:.3f} {cand['h']:.3f} {yaw:.4f} LABEL_A")
        rows.append(" ".join(parts))
    sub_df["PredictionString"] = rows
    sub_df.to_csv("submission.csv", index=False)

P1 — Hard-coded uncertainty term in a likelihood-scored submission

    dw = np.abs(pred_df["weeks"] - pred_df["base_week"])
    conf = 150.0 + 3.0 * dw          # hand-picked base and slope
    conf = np.maximum(conf, 70.0)
    out = pd.DataFrame({
        "row_id": pred_df["row_id"],
        "value": preds,
        "Confidence": conf,
    })
    out.to_csv("submission.csv", index=False)

    The sigma dimension of the likelihood metric is uncalibrated: on the
    contrastive pair, the hard-coded-confidence run scored ~5.6% worse on
    the official metric than the same pipeline with the base/slope
    grid-searched against the metric on out-of-fold predictions.

    best = (None, -1e18)
    for a in range(60, 300, 10):
        for b in np.arange(0.0, 8.0, 0.5):
            sigma = np.maximum(a + b * oof_dw, 70.0)
            score = metric(oof_true, oof_pred, sigma)
            if score > best[1]:
                best = ((a, b), score)
    a, b = best[0]
    conf = np.maximum(a + b * dw, 70.0)
    out = pd.DataFrame({"row_id": ids, "value": preds, "Confidence": conf})
    out.to_csv("submission.csv", index=False)

P1 — Constant per-row uncertainty despite horizon covariate

    sub = sample_sub.merge(base_small, on="id_a", how="left")
    sub["weeks_since_base"] = sub["week"] - sub["week_base"]
    sub["pred"] = model.predict(make_features(sub))
    best_sigma = fit_global_sigma(train_residuals)
    sub["Confidence"] = best_sigma
    sub[["row_id", "pred", "Confidence"]].to_csv("submission.csv", index=False)

    Averaged likelihood metric drops (~0.03 worse) versus horizon-scaled
    uncertainty: far-horizon rows with large true error are penalized by an
    under-sized sigma, while near-horizon rows waste an over-sized sigma.

    sub = sample_sub.merge(base_small, on="id_a", how="left")
    sub["weeks_since_base"] = sub["week"] - sub["week_base"]
    sub["pred"] = model.predict(make_features(sub))
    conf = a + b * np.abs(sub["weeks_since_base"].astype(float))
    sub["Confidence"] = np.maximum(conf, min_sigma)
    sub[["row_id", "pred", "Confidence"]].to_csv("submission.csv", index=False)

P1 — Test-set imputation statistics diverge from training imputation

    X = train_df.drop(columns=["label"])
    y = train_df["label"]
    X_test = test_df.copy()

    X = X.fillna(X.mean())
    X_test = X_test.fillna(X_test.mean())

    model.fit(X, y)
    preds = model.predict(X_test)

    Columns with missing values are filled with different constants at train
    and inference time; test rows are shifted relative to the distribution the
    model learned, lowering held-out accuracy (observed -0.023 on the
    evaluation metric versus reusing training-set statistics).

    X = train_df.drop(columns=["label"])
    y = train_df["label"]
    X_test = test_df.copy()

    fill_values = X.mean()
    X = X.fillna(fill_values)
    X_test = X_test.fillna(fill_values)

    model.fit(X, y)
    preds = model.predict(X_test)

P1 — Row-identifier column used as a model feature

    import pandas as pd
    from sklearn.ensemble import RandomForestClassifier
    train = pd.read_csv('train.csv')
    test = pd.read_csv('test.csv')
    y = train['target']
    X = train.drop('target', axis=1)      # 'Id' column still in X
    model = RandomForestClassifier(n_estimators=100, random_state=42)
    model.fit(X, y)
    preds = model.predict(test.drop(columns=[]))  # 'Id' still in features
    pd.DataFrame({'Id': test['Id'], 'target': preds}).to_csv('sub.csv', index=False)

    Trees split on the identifier column; test rows have identifier values above
    the training range, so they all fall into the same branch of those splits.
    Held-out accuracy drops (observed ~2.4% relative deficit versus an identical
    pipeline that excludes the identifier), with predictions systematically
    biased toward the classes dominant in the highest-Id training rows.

    import pandas as pd
    from sklearn.ensemble import RandomForestClassifier
    train = pd.read_csv('train.csv')
    test = pd.read_csv('test.csv')
    feats = [c for c in train.columns if c not in ('Id', 'target')]
    y = train['target']
    model = RandomForestClassifier(n_estimators=100, random_state=42)
    model.fit(train[feats], y)
    preds = model.predict(test[feats])
    pd.DataFrame({'Id': test['Id'], 'target': preds}).to_csv('sub.csv', index=False)

P1 — Test-frame-derived imputation statistics

    train_df = pd.read_csv('train.csv')
    test_df = pd.read_csv('test.csv')
    X = train_df.drop('LABEL_A', axis=1).fillna(train_df.mean(numeric_only=True))
    y = train_df['LABEL_A']
    X_test = test_df.fillna(test_df.mean(numeric_only=True))
    model = RandomForestClassifier(n_estimators=100).fit(X, y)
    preds = model.predict(X_test[X.columns])

    Rows in the test frame containing missing values receive imputed inputs
    shifted relative to the training-time fill values; held-out accuracy drops
    (~0.02 on the evaluation metric in measured runs), concentrated on rows
    with missing entries.

    train_df = pd.read_csv('train.csv')
    test_df = pd.read_csv('test.csv')
    fill_values = train_df.drop('LABEL_A', axis=1).mean(numeric_only=True)
    X = train_df.drop('LABEL_A', axis=1).fillna(fill_values)
    y = train_df['LABEL_A']
    X_test = test_df.fillna(fill_values)
    model = RandomForestClassifier(n_estimators=100).fit(X, y)
    preds = model.predict(X_test[X.columns])

P1 — Zero-block stand-in for missing feature source before scaling

    def build_features(sample_dir):
        blocks = []
        for src in ["src_a", "src_b", "src_c"]:
            path = sample_dir / f"{src}.npy"
            if path.exists():
                blocks.append(summarize(np.load(path)))   # 8 stats
            else:
                blocks.append(np.zeros(8))                # missing source
        return np.concatenate(blocks)

    X = np.stack([build_features(d) for d in sample_dirs])
    scaler = StandardScaler()
    X_scaled = scaler.fit_transform(X)

    Samples missing a source get standardized values several standard
    deviations from the column mean (zeros masquerading as real extreme
    measurements); the model fits these artifacts and the held-out
    classification metric drops by roughly 0.08 relative to imputing
    missing blocks from training-set statistics.

    def build_features(sample_dir):
        blocks = []
        for src in ["src_a", "src_b", "src_c"]:
            path = sample_dir / f"{src}.npy"
            if path.exists():
                blocks.append(summarize(np.load(path)))
            else:
                blocks.append(np.full(8, np.nan))         # sentinel
        return np.concatenate(blocks)

    imp = SimpleImputer(strategy="median")
    X_train = imp.fit_transform(np.stack([build_features(d) for d in train_dirs]))
    X_test = imp.transform(np.stack([build_features(d) for d in test_dirs]))

P1 — Test-frame imputation uses test-derived statistics

    train_df = pd.read_csv('train.csv')
    test_df = pd.read_csv('test.csv')
    X_train = train_df.drop('label', axis=1).fillna(
        train_df.mean(numeric_only=True))
    y_train = train_df['label']
    X_test = test_df.fillna(test_df.mean(numeric_only=True))
    model.fit(X_train, y_train)
    preds = model.predict(X_test)

    Test rows containing missing values are filled from the test frame's own
    column means, which differ from the training means the model was fitted
    against; affected feature values shift distribution and held-out accuracy
    on those rows drops (observed ~0.02 lower on the evaluation metric).

    train_df = pd.read_csv('train.csv')
    test_df = pd.read_csv('test.csv')
    fill_values = train_df.mean(numeric_only=True)
    X_train = train_df.drop('label', axis=1).fillna(fill_values)
    y_train = train_df['label']
    X_test = test_df.fillna(fill_values)
    model.fit(X_train, y_train)
    preds = model.predict(X_test)

P1 — One-vs-rest incremental linear classifier under extreme class count

    from sklearn.linear_model import SGDClassifier
    import numpy as np

    all_classes = np.unique(labels)   # ~5000 distinct labels
    clf = SGDClassifier(loss="log_loss")
    BATCH = 512
    for X_batch, y_batch in iter_minibatches(feats, labels, BATCH):
        clf.partial_fit(X_batch, y_batch, classes=all_classes)
    preds = clf.predict(X_test)

    Each of the ~5000 per-class binary learners sees on average ~0.1 positive
    examples per 512-row update, so weights barely move off the negative
    class; top-1 accuracy lands near the majority-class baseline, roughly
    6x lower than a single softmax model trained on the same features.

    from sklearn.linear_model import SGDClassifier
    import numpy as np

    all_classes = np.unique(labels)   # 10 distinct labels
    clf = SGDClassifier(loss="log_loss")
    BATCH = 512
    for X_batch, y_batch in iter_minibatches(feats, labels, BATCH):
        clf.partial_fit(X_batch, y_batch, classes=all_classes)
    preds = clf.predict(X_test)

P1 — Crippled boosted-ensemble capacity without validation-based selection

    import lightgbm as lgb
    model = lgb.LGBMClassifier(
        n_estimators=10,
        learning_rate=0.5,
        max_depth=3,
        num_leaves=7,
        max_bin=31,
        random_state=42,
    )
    model.fit(X, y)
    preds = model.predict(X_test)

    Held-out accuracy drops ~10% relative to the same model at default capacity
    (100 estimators, 31 leaves): the ensemble underfits, plateauing far below the
    achievable score while consuming only a small fraction of the time budget.

    import lightgbm as lgb
    model = lgb.LGBMClassifier(
        n_estimators=1000,
        learning_rate=0.05,
        num_leaves=31,
        random_state=42,
    )
    model.fit(X_tr, y_tr, eval_set=[(X_val, y_val)],
              callbacks=[lgb.early_stopping(50)])
    preds = model.predict(X_test)

P1 — Blind fit with no validation-guided capacity selection

    train_df = pd.read_csv('train.csv')
    test_df = pd.read_csv('test.csv')
    X = train_df.drop(columns=['label'])
    y = train_df['label']
    model = RandomForestClassifier(n_estimators=100, max_depth=20,
                                   min_samples_leaf=2, random_state=42)
    model.fit(X, y)
    preds = model.predict(test_df)
    pd.DataFrame({'id': test_df['id'], 'label': preds}).to_csv('out.csv', index=False)

    Final test accuracy is systematically below what the same compute achieves
    with validation-guided capacity selection: ~2% relative accuracy lost
    versus an early-stopped boosted model of comparable training cost, with
    no signal in the run indicating the model is under- or over-fitted.

    X_tr, X_val, y_tr, y_val = train_test_split(X, y, test_size=0.1, random_state=42)
    train_set = lgb.Dataset(X_tr, label=y_tr)
    val_set = lgb.Dataset(X_val, label=y_val, reference=train_set)
    model = lgb.train(params, train_set, num_boost_round=1000,
                      valid_sets=[val_set],
                      callbacks=[lgb.early_stopping(stopping_rounds=30)])
    preds = model.predict(X_test, num_iteration=model.best_iteration)

P1 — Default regularization on high-dimensional linear probability model

    X = np.array([extract_hog(p) for p in train_paths], dtype=np.float32)  # ~8000 dims
    scaler = StandardScaler()
    Xs = scaler.fit_transform(X)

    clf = LogisticRegression(max_iter=1000)   # default C=1.0, no tuning
    clf.fit(Xs, y)

    Xt = scaler.transform(np.array([extract_hog(p) for p in test_paths]))
    probs = clf.predict_proba(Xt)
    pd.DataFrame(probs, columns=clf.classes_).to_csv("submission.csv", index=False)

    The model overfits the underdetermined feature space and emits sharply
    peaked, poorly calibrated probabilities; log loss on held-out data is
    roughly 2-3x worse than a strongly regularized fit of the same model
    (observed: 11.92 vs 4.55 on the same features).

    X = np.array([extract_hog(p) for p in train_paths], dtype=np.float32)
    Xs = StandardScaler().fit_transform(X)

    best_c, best_loss = None, np.inf
    for c in [0.001, 0.01, 0.1, 1.0]:
        m = LogisticRegression(C=c, max_iter=1000)
        loss = -cross_val_score(m, Xs, y, scoring="neg_log_loss", cv=3).mean()
        if loss < best_loss:
            best_c, best_loss = c, loss
    clf = LogisticRegression(C=best_c, max_iter=1000).fit(Xs, y)

P1 — Hardcoded tiny training subset with sequential per-file feature extraction

    files = sorted(os.listdir(train_dir))[:900]
    feats = []
    labels = []
    for fname in files:
        f = extract_features(os.path.join(train_dir, fname))
        feats.append(f)
        labels.append(label_for(fname))
    X = np.vstack(feats)
    y = np.array(labels)
    model.fit(X, y)

    Held-out metric drops from 0.82 to 0.58 (-0.29) versus the same pipeline
    trained on 10x more files, which fits within the same wall-clock budget
    when extraction is parallelized; the run exits cleanly either way.

    files = sorted(os.listdir(train_dir))
    n = min(len(files), int(budget_seconds / est_sec_per_file * os.cpu_count()))
    subset = files[:n]
    paths = [os.path.join(train_dir, f) for f in subset]
    with Pool(os.cpu_count()) as pool:
        feats = pool.map(extract_features, paths)
    X = np.vstack(feats)
    model.fit(X, np.array([label_for(f) for f in subset]))

P1 — Early-stopped model predicts test without refit on full training data

    Xtr, Xva, ytr, yva = train_test_split(X, y, test_size=0.15, random_state=42)
    dtr = lgb.Dataset(Xtr, label=ytr)
    dva = lgb.Dataset(Xva, label=yva, reference=dtr)
    model = lgb.train(
        params, dtr, num_boost_round=3000, valid_sets=[dva],
        callbacks=[lgb.early_stopping(100)],
    )
    preds = model.predict(X_test, num_iteration=model.best_iteration)
    sub = pd.DataFrame({"Id": ids, "Label": preds})
    sub.to_csv("submission.csv", index=False)

    Final model is fit on ~85% of the labeled rows; the test-set metric is
    measurably lower (observed gap ~0.14 on the evaluation metric) than a
    model refit on 100% of the labeled data at the selected iteration count.

    Xtr, Xva, ytr, yva = train_test_split(X, y, test_size=0.15, random_state=42)
    dtr = lgb.Dataset(Xtr, label=ytr)
    dva = lgb.Dataset(Xva, label=yva, reference=dtr)
    model = lgb.train(
        params, dtr, num_boost_round=3000, valid_sets=[dva],
        callbacks=[lgb.early_stopping(100)],
    )
    best_it = model.best_iteration or 500
    final = lgb.train(params, lgb.Dataset(X, label=y),
                      num_boost_round=max(50, int(best_it * 1.1)))
    preds = final.predict(X_test)

P1 — CV metric printed but never used for model selection

    skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)
    oof = np.zeros(len(y))
    test_pred = np.zeros(len(X_test))
    for tr, va in skf.split(X, y):
        m = make_pipeline(StandardScaler(),
                          LogisticRegression(C=0.01, max_iter=2000))
        m.fit(X[tr], y[tr])
        oof[va] = m.predict_proba(X[va])[:, 1]
        test_pred += m.predict_proba(X_test)[:, 1] / 5
    print("OOF AUC:", roc_auc_score(y, oof))
    sub["LABEL_A"] = test_pred

    Held-out AUC lands near chance (~0.50) while a small grid over
    regularization strengths plus one boosted-tree alternative, selected
    by the same OOF metric, reaches ~0.58 — a ~13% relative deficit —
    with the run using ~2 minutes of a multi-hour compute budget.

    best_score, best_cfg = -1, None
    for C in [0.01, 0.1, 1.0, 10.0]:
        oof = np.zeros(len(y))
        for tr, va in skf.split(X, y):
            m = make_pipeline(StandardScaler(), LogisticRegression(C=C))
            m.fit(X[tr], y[tr])
            oof[va] = m.predict_proba(X[va])[:, 1]
        score = roc_auc_score(y, oof)
        if score > best_score:
            best_score, best_cfg = score, C
    final = make_pipeline(StandardScaler(),
                          LogisticRegression(C=best_cfg)).fit(X, y)

P1 — Small hard vocabulary cap on sparse-text features for a linear model

    from sklearn.feature_extraction.text import TfidfVectorizer
    from sklearn.linear_model import LogisticRegression

    vec = TfidfVectorizer(max_features=5000, stop_words='english')
    X_tr = vec.fit_transform(df_train['col_a'])
    X_te = vec.transform(df_test['col_a'])

    model = LogisticRegression(max_iter=1000)
    model.fit(X_tr, y_train)
    probs = model.predict_proba(X_te)

    Log loss on the held-out set worsens from ~0.35 (uncapped vocabulary
    with min_df pruning) to ~0.58, because rare but strongly
    class-indicative n-grams are excluded from the 5000-term vocabulary.

    from sklearn.feature_extraction.text import TfidfVectorizer
    from sklearn.linear_model import LogisticRegression

    vec = TfidfVectorizer(min_df=3, max_features=100000,
                          stop_words='english')
    X_tr = vec.fit_transform(df_train['col_a'])
    X_te = vec.transform(df_test['col_a'])

    model = LogisticRegression(C=4.0, max_iter=1000)
    model.fit(X_tr, y_train)
    probs = model.predict_proba(X_te)

P1 — Untuned single default-regularized classifier on a probability metric

    vec = TfidfVectorizer(max_features=10000, stop_words='english')
    X_tr = vec.fit_transform(df['col_a'])
    X_te = vec.transform(test['col_a'])

    model = LogisticRegression(max_iter=1000, random_state=42)
    model.fit(X_tr, y)
    proba = model.predict_proba(X_te)

    sub = pd.DataFrame(proba, columns=classes)
    sub.insert(0, 'id', test['id'])
    sub.to_csv('submission.csv', index=False)

    Log loss on the held-out evaluation is substantially higher (observed gap
    ~0.38) than an equally cheap run that tuned the regularization strength
    and blended a second model family, leaving most of the available
    training-time budget unused.

    lr = LogisticRegression(C=4, max_iter=1000)
    lr.fit(X_tr, y)
    p_lr = lr.predict_proba(X_te)

    nb = MultinomialNB(alpha=0.05)
    nb.fit(X_tr, y)
    p_nb = nb.predict_proba(X_te)

    proba = 0.7 * p_lr + 0.3 * p_nb
    sub = pd.DataFrame(proba, columns=classes)
    sub.insert(0, 'id', test['id'])
    sub.to_csv('submission.csv', index=False)

P1 — Small hardcoded vocabulary cap on a large text corpus

    from sklearn.feature_extraction.text import TfidfVectorizer
    from sklearn.linear_model import LogisticRegression

    vec = TfidfVectorizer(max_features=3000, ngram_range=(1, 2))
    X_train = vec.fit_transform(train["text"])
    X_test = vec.transform(test["text"])

    model = LogisticRegression(max_iter=1000)
    model.fit(X_train, y_train)
    probs = model.predict_proba(X_test)

    Held-out log loss is systematically higher (e.g. ~0.4 worse) than the same
    pipeline with the cap removed or raised to tens of thousands of features,
    because rare but discriminative n-grams are truncated from the vocabulary;
    training time remains seconds either way, so the cap buys nothing.

    from sklearn.feature_extraction.text import TfidfVectorizer
    from sklearn.linear_model import LogisticRegression
    from scipy.sparse import hstack

    word_vec = TfidfVectorizer(ngram_range=(1, 2), sublinear_tf=True)
    char_vec = TfidfVectorizer(analyzer="char_wb", ngram_range=(2, 5),
                               max_features=50000)
    X_train = hstack([word_vec.fit_transform(train["text"]),
                      char_vec.fit_transform(train["text"])])
    model = LogisticRegression(max_iter=1000).fit(X_train, y_train)

P1 — Unvalidated high-capacity boosted ensemble on small handcrafted-feature set

    X, y = [], []
    for sample_id in train_ids:
        X.append(extract_stats(sample_id))   # handcrafted stats per sample
        y.append(labels[sample_id])
    X = scaler.fit_transform(np.array(X))    # ~400 rows
    model = GradientBoostingClassifier(n_estimators=200, max_depth=6)
    model.fit(X, y)
    proba = model.predict_proba(scaler.transform(X_test))[:, 1]
    pd.DataFrame({'id': test_ids, 'LABEL_A': proba}).to_csv('out.csv', index=False)

    The ensemble memorizes feature noise on the tiny training set; held-out
    ranking score drops to ~0.49 (chance level), versus ~0.55 achievable with
    capacity chosen against a held-out estimate — a ~11% relative degradation.

    X, y = [], []
    for sample_id in train_ids:
        X.append(extract_stats(sample_id))
        y.append(labels[sample_id])
    X = scaler.fit_transform(np.array(X))
    grid = {'max_depth': [2, 3], 'min_samples_leaf': [5, 20]}
    search = GridSearchCV(GradientBoostingClassifier(n_estimators=100),
                          grid, cv=5, scoring='roc_auc')
    search.fit(X, y)
    proba = search.best_estimator_.predict_proba(scaler.transform(X_test))[:, 1]
    pd.DataFrame({'id': test_ids, 'LABEL_A': proba}).to_csv('out.csv', index=False)

P1 — Train-once fit with no validation estimate before submission

    df = pd.read_csv("train.csv")
    X = df[feature_cols].values
    y = df["target"].values
    scaler = StandardScaler().fit(X)
    model = RandomForestClassifier(n_estimators=100, random_state=0)
    model.fit(scaler.transform(X), y)
    test = pd.read_csv("test.csv")
    preds = model.predict_proba(scaler.transform(test[feature_cols]))[:, 1]
    pd.DataFrame({"Id": test["Id"], "Label": preds}).to_csv("submission.csv", index=False)

    Model capacity and iteration budget are never selected against held-out
    performance; the scored metric lands measurably below what the same
    pipeline achieves with a validated, early-stopped configuration
    (observed ~10% relative shortfall on the task metric).

    Xtr, Xva, ytr, yva = train_test_split(X, y, test_size=0.2, random_state=0)
    dtr = lgb.Dataset(Xtr, label=ytr)
    dva = lgb.Dataset(Xva, label=yva)
    model = lgb.train(params, dtr, num_boost_round=3000, valid_sets=[dva],
                      callbacks=[lgb.early_stopping(100)])
    print("val AUC:", roc_auc_score(yva, model.predict(Xva)))
    final = lgb.train(params, lgb.Dataset(X, label=y),
                      num_boost_round=model.best_iteration)
    pd.DataFrame({"Id": ids, "Label": final.predict(Xte)}).to_csv("submission.csv", index=False)

P1 — Validation metric computed but never used for hyperparameter selection

    xa, xb, ya, yb = train_test_split(X, y, test_size=0.1, random_state=0)
    clf = LogisticRegression(max_iter=2000, C=1.0)
    clf.fit(xa, ya)
    p = clf.predict_proba(xb)[:, 1]
    print("val logloss:", log_loss(yb, p))

    clf_full = LogisticRegression(max_iter=2000, C=1.0)
    clf_full.fit(X, y)
    preds = clf_full.predict_proba(X_test)[:, 1]

    Final test log loss is ~10% higher (worse) than the same pipeline that
    sweeps C over a small grid, picks the best value on the held-out split,
    and refits on all data; the validation score is computed but wasted.

    xa, xb, ya, yb = train_test_split(X, y, test_size=0.1, random_state=0)
    best_c, best_s = None, float("inf")
    for C in [0.003, 0.01, 0.03, 0.1]:
        m = LogisticRegression(max_iter=1000, C=C)
        m.fit(xa, ya)
        s = log_loss(yb, m.predict_proba(xb)[:, 1])
        if s < best_s:
            best_s, best_c = s, C
    model = LogisticRegression(max_iter=2000, C=best_c)
    model.fit(X, y)
    preds = model.predict_proba(X_test)[:, 1]

P1 — Hardcoded small vocabulary cap on sparse text features feeding a linear model

    from sklearn.feature_extraction.text import TfidfVectorizer
    from sklearn.linear_model import LogisticRegression

    tfidf = TfidfVectorizer(max_features=5000, ngram_range=(1, 2), min_df=2)
    X_tr = tfidf.fit_transform(df["text_col"])
    X_te = tfidf.transform(test_df["text_col"])

    model = LogisticRegression(max_iter=1000)
    model.fit(X_tr, y_train)
    proba = model.predict_proba(X_te)

    Multiclass log loss rises from ~0.33 (full vocabulary) to ~0.54 with the
    5000-feature cap: rare but class-discriminative terms are dropped, so
    predicted probabilities are systematically less confident on the correct
    class. Direction: evaluation metric worsens by ~38% relative.

    from sklearn.feature_extraction.text import TfidfVectorizer
    from sklearn.linear_model import LogisticRegression

    tfidf = TfidfVectorizer(ngram_range=(1, 2), min_df=2, sublinear_tf=True)
    X_tr = tfidf.fit_transform(df["text_col"])
    X_te = tfidf.transform(test_df["text_col"])

    model = LogisticRegression(C=2.0, max_iter=1000)
    model.fit(X_tr, y_train)
    proba = model.predict_proba(X_te)

P1 — Single default-configured probabilistic model with no ensembling or validation

    tfidf = TfidfVectorizer(max_features=5000)
    X_tr = tfidf.fit_transform(train_df["col_a"])
    X_te = tfidf.transform(test_df["col_a"])

    model = LogisticRegression(max_iter=1000, random_state=42)
    model.fit(X_tr, y_enc)
    proba = model.predict_proba(X_te)

    sub = pd.DataFrame(proba, columns=classes)
    sub.insert(0, "id", test_df["id"].values)
    sub.to_csv("submission.csv", index=False)

    Multiclass log loss is substantially higher (worse) than a same-budget
    blend of several cheap diverse classifiers: 0.54 vs 0.30 on identical
    features, while using ~8% of the wall-clock the stronger run needed and
    a tiny fraction of the overall runtime budget.

    models = [LogisticRegression(max_iter=2000),
              MultinomialNB(alpha=0.5)]
    oof = np.zeros((X_tr.shape[0], n_classes)); preds = []
    for m in models:
        for tr, va in skf.split(X_tr, y):
            m.fit(X_tr[tr], y[tr])
            oof[va] += m.predict_proba(X_tr[va]) / len(models)
        m.fit(X_tr, y)
        preds.append(m.predict_proba(X_te))
    print("oof loss:", log_loss(y, oof))
    pd.DataFrame(np.mean(preds, axis=0), columns=classes).to_csv("submission.csv", index=False)

P1 — Validation set supplied to boosting fit without early stopping

    for tr_idx, va_idx in skf.split(X, y):
        model = LGBMClassifier(
            n_estimators=1000,
            learning_rate=0.03,
        )
        model.fit(
            X[tr_idx], y[tr_idx],
            eval_set=[(X[va_idx], y[va_idx])],
            eval_metric="auc",
            callbacks=None,
        )
        oof[va_idx] = model.predict_proba(X[va_idx])[:, 1]

    Every fold trains all 1000 boosting rounds regardless of validation
    performance; the model overfits past the validation optimum on noisy
    features, lowering held-out AUC (~0.20 lower in a measured pair) and
    spending several times the necessary training compute per fold.

    for tr_idx, va_idx in skf.split(X, y):
        model = LGBMClassifier(
            n_estimators=3000,
            learning_rate=0.03,
        )
        model.fit(
            X[tr_idx], y[tr_idx],
            eval_set=[(X[va_idx], y[va_idx])],
            eval_metric="auc",
            callbacks=[lgb.early_stopping(100, verbose=False)],
        )
        oof[va_idx] = model.predict_proba(X[va_idx])[:, 1]

P1 — Low vocabulary cap starving a sparse linear text model

    from sklearn.feature_extraction.text import TfidfVectorizer
    from sklearn.linear_model import LogisticRegression

    vec = TfidfVectorizer(max_features=5000, stop_words='english')
    X_tr = vec.fit_transform(train['col_a'])
    X_te = vec.transform(test['col_a'])

    model = LogisticRegression(max_iter=1000)
    model.fit(X_tr, y_train)
    preds = model.predict_proba(X_te)

    Log loss on the held-out evaluation rises (e.g. ~0.55 vs ~0.35 for the
    uncapped vocabulary) because the linear model is denied tens of thousands
    of discriminative rare terms; wall-clock time is not reduced, so the
    truncation trades accuracy for nothing.

    from sklearn.feature_extraction.text import TfidfVectorizer
    from sklearn.linear_model import LogisticRegression

    vec = TfidfVectorizer(min_df=2, max_df=0.95, stop_words='english')
    X_tr = vec.fit_transform(train['col_a'])
    X_te = vec.transform(test['col_a'])

    model = LogisticRegression(max_iter=1000)
    model.fit(X_tr, y_train)
    preds = model.predict_proba(X_te)

P1 — Small capped vocabulary as sole text representation

    from sklearn.feature_extraction.text import TfidfVectorizer
    from sklearn.linear_model import LogisticRegression

    vectorizer = TfidfVectorizer(max_features=5000, ngram_range=(1, 2))
    X_tr = vectorizer.fit_transform(train_df["col_a"])
    X_te = vectorizer.transform(test_df["col_a"])

    model = LogisticRegression(max_iter=1000)
    model.fit(X_tr, y_train)
    preds = model.predict_proba(X_te)

    Held-out log loss is ~0.19 absolute worse than the same model trained on
    an uncapped word + character n-gram representation; runtime (221s) was
    far below the allowed budget (richer variant fits in 581s), so the
    vocabulary restriction sacrificed accuracy for headroom that was unused.

    from sklearn.feature_extraction.text import TfidfVectorizer
    from scipy.sparse import hstack
    from sklearn.linear_model import LogisticRegression

    word_vec = TfidfVectorizer(ngram_range=(1, 2), sublinear_tf=True)
    char_vec = TfidfVectorizer(analyzer="char_wb", ngram_range=(2, 5))
    X_tr = hstack([word_vec.fit_transform(train_df["col_a"]),
                   char_vec.fit_transform(train_df["col_a"])]).tocsr()
    model = LogisticRegression(max_iter=1000)
    model.fit(X_tr, y_train)

P1 — Small vocabulary cap on sparse text features with an untuned single linear model

    vectorizer = TfidfVectorizer(
        max_features=5000,
        ngram_range=(1, 2),
        stop_words='english'
    )
    X_tr = vectorizer.fit_transform(train_text)
    X_te = vectorizer.transform(test_text)

    model = LogisticRegression(max_iter=1000, random_state=42)
    model.fit(X_tr, y_train)
    proba = model.predict_proba(X_te)

    Held-out log loss ~0.58 versus ~0.38 achievable with the full vocabulary
    and tuned regularization on the same data — a ~33% relative degradation —
    while using roughly one third of the available wall-clock budget.

    vectorizer = TfidfVectorizer(
        max_features=200000,
        ngram_range=(1, 2),
        sublinear_tf=True
    )
    X_tr = vectorizer.fit_transform(train_text)
    X_te = vectorizer.transform(test_text)

    grid = GridSearchCV(LogisticRegression(max_iter=1000),
                        {'C': [0.1, 1.0, 10.0]}, scoring='neg_log_loss', cv=5)
    grid.fit(X_tr, y_train)
    proba = grid.predict_proba(X_te)

P1 — Linear head on a projection far narrower than the label space

    NUM_CLASSES = 5270          # thousands of labels
    pca = PCA(n_components=100)
    Xtr = pca.fit_transform(X)  # X is 4096-dim features
    clf = LogisticRegression(max_iter=200)
    clf.fit(Xtr, y)
    preds = clf.predict(pca.transform(X_test))

    Held-out accuracy is substantially lower (observed gap ~0.69 on the task
    metric) than a same-budget linear model fitted on the unreduced features:
    a 100-dimensional linear decision space cannot separate 5000+ classes,
    so many classes collapse onto the same decision regions.

    NUM_CLASSES = 5270
    pca = PCA(n_components=256)
    Xtr = pca.fit_transform(X)
    clf = MLPClassifier(hidden_layer_sizes=(1024,))
    clf.fit(Xtr, y)
    preds = clf.predict(pca.transform(X_test))

P1 — Tiny boosting budget masked by inflated learning rate

    import lightgbm as lgb

    model = lgb.LGBMClassifier(
        n_estimators=10,
        learning_rate=0.5,
        max_depth=3,
        random_state=42,
    )
    model.fit(X, y)
    preds = model.predict(X_test)

    Training loss plateaus after only a handful of coarse, high-step updates;
    held-out accuracy lands roughly 0.10 below what the same wall-clock budget
    achieves with learning_rate=0.1 and n_estimators=100.

    import lightgbm as lgb

    model = lgb.LGBMClassifier(
        n_estimators=100,
        learning_rate=0.1,
        max_depth=3,
        random_state=42,
    )
    model.fit(X, y)
    preds = model.predict(X_test)

P1 — Small hardcoded vocabulary cap on sparse text features for a linear model

    from sklearn.feature_extraction.text import TfidfVectorizer
    from sklearn.linear_model import LogisticRegression

    tfidf = TfidfVectorizer(max_features=5000, ngram_range=(1, 2), min_df=2)
    X_tr = tfidf.fit_transform(train_df["col_a"])
    X_te = tfidf.transform(test_df["col_a"])

    model = LogisticRegression(max_iter=1000)
    model.fit(X_tr, y_train)
    probs = model.predict_proba(X_te)

    Held-out log loss rises from ~0.30 (full vocabulary) to ~0.54 with the
    5000-feature cap; most discriminative rare n-grams are discarded while
    total runtime stays far below the available budget (~6s vs ~64s used
    by the uncapped pipeline).

    from sklearn.feature_extraction.text import TfidfVectorizer
    from sklearn.linear_model import LogisticRegression

    tfidf = TfidfVectorizer(ngram_range=(1, 2), min_df=2, sublinear_tf=True)
    X_tr = tfidf.fit_transform(train_df["col_a"])
    X_te = tfidf.transform(test_df["col_a"])

    model = LogisticRegression(max_iter=1000)
    model.fit(X_tr, y_train)
    probs = model.predict_proba(X_te)

P1 — Validation score computed but never used to select anything

    Xa, Xb, ya, yb = train_test_split(X, y, test_size=0.15, random_state=42)
    clf = LogisticRegression(max_iter=2000, C=1.0)
    clf.fit(Xa, ya)
    val_ll = log_loss(yb, clf.predict_proba(Xb)[:, 1])
    print("val logloss:", val_ll)

    clf_full = LogisticRegression(max_iter=2000, C=1.0)
    clf_full.fit(X, y)
    preds = clf_full.predict_proba(X_test)[:, 1]
    pd.DataFrame({"id": ids, "label": preds}).to_csv("out.csv", index=False)

    Final held-out log loss is systematically higher than a run that tried a
    small grid of C values on the same validation split; observed gap ~6%
    relative log-loss deficit with the untuned default regularization.

    Xa, Xb, ya, yb = train_test_split(X, y, test_size=0.15, random_state=42)
    best_C, best_ll = None, None
    for C in [0.003, 0.01, 0.03, 0.1, 1.0]:
        clf = LogisticRegression(max_iter=500, C=C)
        clf.fit(Xa, ya)
        ll = log_loss(yb, clf.predict_proba(Xb)[:, 1])
        if best_ll is None or ll < best_ll:
            best_ll, best_C = ll, C
    clf_full = LogisticRegression(max_iter=500, C=best_C)
    clf_full.fit(X, y)
    preds = clf_full.predict_proba(X_test)[:, 1]

P1 — Small hard vocabulary cap with untuned classifier regularization

    from sklearn.feature_extraction.text import TfidfVectorizer
    from sklearn.linear_model import LogisticRegression

    vec = TfidfVectorizer(max_features=5000, ngram_range=(1, 2))
    X_tr = vec.fit_transform(train_df["col_a"])
    X_te = vec.transform(test_df["col_a"])

    model = LogisticRegression(max_iter=1000, random_state=42)
    model.fit(X_tr, y_train)
    proba = model.predict_proba(X_te)

    Log loss on the held-out set is materially higher (~38% relative worse in the
    measured pair) than an identical pipeline with the vocabulary cap removed and
    regularization strength tuned; wall-clock time stays far under the available
    budget, so the discarded capacity buys nothing.

    from sklearn.feature_extraction.text import TfidfVectorizer
    from sklearn.linear_model import LogisticRegression

    vec = TfidfVectorizer(min_df=3, sublinear_tf=True, ngram_range=(1, 2))
    X_tr = vec.fit_transform(train_df["col_a"])
    X_te = vec.transform(test_df["col_a"])

    model = LogisticRegression(C=4, max_iter=1000)
    model.fit(X_tr, y_train)
    proba = model.predict_proba(X_te)

P1 — Starved boosting budget without validation-based sizing

    import lightgbm as lgb
    model = lgb.LGBMClassifier(
        n_estimators=10,
        learning_rate=0.5,
        max_depth=3,
        num_leaves=7,
        random_state=42,
    )
    model.fit(X, y)
    preds = model.predict(X_test)

    Held-out accuracy drops materially (observed ~0.09 absolute, ~9.6%
    relative) compared with a moderately sized ensemble trained on the
    same data in a similar time budget; the model systematically
    underfits and predictions cluster on majority classes.

    import lightgbm as lgb
    model = lgb.LGBMClassifier(
        n_estimators=500,
        learning_rate=0.1,
        num_leaves=48,
        random_state=42,
    )
    model.fit(X_tr, y_tr, eval_set=[(X_val, y_val)],
              callbacks=[lgb.early_stopping(30)])
    preds = model.predict(X_test)

P1 — Default regularization on high-dimensional probabilistic linear model

    from sklearn.linear_model import LogisticRegression
    from sklearn.preprocessing import StandardScaler

    # X: (n_samples, 8000) handcrafted descriptor features
    scaler = StandardScaler()
    X_scaled = scaler.fit_transform(X)
    X_test_scaled = scaler.transform(X_test)

    clf = LogisticRegression(max_iter=1000)
    clf.fit(X_scaled, y)
    probs = clf.predict_proba(X_test_scaled)
    pd.DataFrame(probs, columns=classes).to_csv("submission.csv", index=False)

    The near-interpolating fit emits sharply peaked probability vectors that are
    frequently wrong; log loss rises to ~11.6 versus ~4.6 for the same features
    with strong regularization — a 0.6+ degradation on the probability metric.

    from sklearn.linear_model import LogisticRegression
    from sklearn.preprocessing import StandardScaler

    scaler = StandardScaler()
    X_scaled = scaler.fit_transform(X)
    X_test_scaled = scaler.transform(X_test)

    clf = LogisticRegression(C=0.01, max_iter=1000)
    clf.fit(X_scaled, y)
    probs = clf.predict_proba(X_test_scaled)
    probs = 0.9 * probs + 0.1 / probs.shape[1]
    pd.DataFrame(probs, columns=classes).to_csv("submission.csv", index=False)

P1 — Global-cap subsampling without per-class quota in extreme multiclass

    CAP = 60000
    X, y = [], []
    kept = 0
    for rec in stream_records(TRAIN_FILE):
        if kept >= CAP:
            break
        X.append(featurize(rec["img"]))
        y.append(rec["label"])       # ~5000 distinct labels overall
        kept += 1
    model.fit(np.array(X), np.array(y))

    Average examples per class falls to ~12, and because the stream is not
    label-balanced most classes receive 0-3 examples; held-out accuracy is
    roughly half that of the same pipeline with a per-class quota
    (e.g. 0.048 vs 0.093 on the evaluation metric).

    QUOTA, CAP = 40, 200000
    counts = {}
    X, y, kept = [], [], 0
    for rec in stream_records(TRAIN_FILE):
        if kept >= CAP:
            break
        lbl = rec["label"]
        if counts.get(lbl, 0) >= QUOTA:
            continue
        counts[lbl] = counts.get(lbl, 0) + 1
        X.append(featurize(rec["img"])); y.append(lbl); kept += 1
    model.fit(np.array(X), np.array(y))

P1 — Boosting fit with eval_set but no early stopping

    model = LGBMClassifier(
        n_estimators=1000,
        learning_rate=0.03,
        num_leaves=31,
    )
    model.fit(
        X_tr, y_tr,
        eval_set=[(X_va, y_va)],
        eval_metric="auc",
        callbacks=None,
    )

    Every fold boosts all 1000 rounds past the validation optimum; held-out
    AUC drops (observed ~0.28 lower) relative to stopping at the best
    iteration, and training wall-clock roughly doubles.

    model = LGBMClassifier(
        n_estimators=1000,
        learning_rate=0.03,
        num_leaves=31,
    )
    model.fit(
        X_tr, y_tr,
        eval_set=[(X_va, y_va)],
        eval_metric="auc",
        callbacks=[lgb.early_stopping(60, verbose=False)],
    )

P1 — Per-row model inference inside a Python loop

    predictions = []
    for fname in test_files:
        features = extract_features(os.path.join(test_dir, fname))
        features_scaled = scaler.transform([features])
        pred = model.predict_proba(features_scaled)[0][1]
        predictions.append((fname, pred))
    sub = pd.DataFrame(predictions, columns=["Id", "Label"])
    sub.to_csv(output_path, index=False)

    Inference wall-clock time grows by roughly an order of magnitude versus a
    single batched transform/predict on the stacked matrix, because per-call
    overhead (validation, dispatch, memory allocation) is paid once per sample
    instead of once per dataset; total runtime increases with no accuracy gain.

    rows = []
    for fname in test_files:
        rows.append(extract_features(os.path.join(test_dir, fname)))
    X_test = np.vstack(rows)
    X_test = scaler.transform(X_test)
    preds = model.predict_proba(X_test)[:, 1]
    sub = pd.DataFrame({"Id": test_files, "Label": preds})
    sub.to_csv(output_path, index=False)

P1 — Per-file serial extraction with per-sample inference calls

    test_files = sorted(f for f in os.listdir(test_dir) if f.endswith('.jpg'))
    predictions = []
    for fname in test_files:
        features = extract_features(os.path.join(test_dir, fname))
        features_scaled = scaler.transform([features])
        pred = model.predict_proba(features_scaled)[0][1]
        predictions.append((fname, pred))
    sub = pd.DataFrame(predictions, columns=['id', 'score'])
    sub.to_csv('submission.csv', index=False)

    Wall-clock time for extraction plus inference rises roughly 7x (e.g. 806s
    vs 108s on the same file set); within a fixed compute budget, ~2.3x fewer
    files can be processed, shrinking the usable training/evaluation set and
    degrading the final metric.

    test_files = sorted(f for f in os.listdir(test_dir) if f.endswith('.jpg'))
    paths = [os.path.join(test_dir, f) for f in test_files]
    with ProcessPoolExecutor() as ex:
        feats = list(ex.map(extract_features, paths))
    X = scaler.transform(np.vstack(feats))
    preds = model.predict_proba(X)[:, 1]
    sub = pd.DataFrame({'id': test_files, 'score': preds})
    sub.to_csv('submission.csv', index=False)

P1 — Full-resolution array materialization before downscale in per-item image loop

    feats = []
    for rec in records:
        img = Image.open(io.BytesIO(rec["picture"]))
        arr = np.array(img)                    # full-resolution array
        small = skimage.transform.resize(arr, (32, 32))
        feats.append(small.flatten())

    Per-item processing time rises severalfold because each iteration allocates
    and fills a full-resolution array that is immediately thrown away; within a
    fixed runtime budget the loop covers orders of magnitude fewer records
    (observed ~140x fewer training examples at ~50% greater wall-clock),
    lowering the final evaluation metric.

    feats = []
    for rec in records:
        img = Image.open(io.BytesIO(rec["picture"]))
        img.draft("JPEG", (32, 32))
        img = img.resize((32, 32))
        arr = np.asarray(img)                  # only the small array exists
        feats.append(arr.flatten())

P1 — Sequential per-file feature extraction over a large file corpus

    files = [f for f in os.listdir(data_dir) if f.endswith('.npy')]
    features = []
    for fname in files:
        arr = np.load(os.path.join(data_dir, fname))
        features.append([arr.mean(), arr.std(), arr.max(), arr.min()])
    X = np.array(features)

    Feature extraction runs on a single core; wall-clock time for this stage
    grows ~Nx relative to an N-worker pool (e.g. ~1400s instead of ~90s on a
    16-core host), consuming the run budget that would otherwise fund richer
    features, more training rounds, or hyperparameter search.

    def feats(path):
        arr = np.load(path)
        return [arr.mean(), arr.std(), arr.max(), arr.min()]

    files = sorted(glob.glob(os.path.join(data_dir, '*.npy')))
    with ProcessPoolExecutor(max_workers=os.cpu_count()) as ex:
        features = list(ex.map(feats, files, chunksize=64))
    X = np.array(features)

P1 — Raw-file re-parsing inside the epoch loop

    for epoch in range(EPOCHS):
        for doc in iter_records(TRAIN_PATH):
            img = doc.get('imgs')[0].get('picture')
            feats = extract_features(img)
            batch_X.append(feats)
            batch_y.append(doc['label'])
            if len(batch_X) >= BATCH:
                model.partial_fit(np.array(batch_X), batch_y,
                                  classes=all_classes)
                batch_X, batch_y = [], []

    Each epoch re-reads and re-decodes the entire multi-GB raw file, so
    per-epoch wall clock is dominated by I/O and decoding; total runtime
    scales as epochs x full-file decode time, forcing fewer effective
    epochs / samples within the time budget and lowering the final
    evaluation metric relative to a cached-feature run.

    X_list, y_list = [], []
    for doc in iter_records(TRAIN_PATH):
        img = doc.get('imgs')[0].get('picture')
        X_list.append(extract_features(img))
        y_list.append(doc['label'])
    X = np.array(X_list, dtype=np.float32); y = np.array(y_list)
    for epoch in range(EPOCHS):
        order = np.random.permutation(len(X))
        for i in range(0, len(X), BATCH):
            idx = order[i:i+BATCH]
            model.partial_fit(X[idx], y[idx], classes=all_classes)

P1 — Sequential per-file feature extraction over a large image collection

    files = [f for f in os.listdir(IMG_DIR) if f.endswith(".jpg")]
    feats = []
    for fname in files:
        path = os.path.join(IMG_DIR, fname)
        feats.append(extract_features(path))  # decodes + computes descriptors
    X = np.vstack(feats)

    Feature extraction wall-clock scales as 1x-core throughput: ~256s instead
    of ~77s on the same workload with a pool sized to available CPUs (~3.3x
    slower), leaving less budget for hyperparameter search or larger feature
    sets; the downstream metric was 0.056-0.061 worse in matched runs.

    from multiprocessing import Pool
    files = sorted(f for f in os.listdir(IMG_DIR) if f.endswith(".jpg"))
    paths = [os.path.join(IMG_DIR, f) for f in files]
    with Pool(os.cpu_count()) as pool:
        feats = pool.map(extract_features, paths)
    X = np.vstack(feats)

P1 — Sequential per-file feature extraction on large file sets

    import os, numpy as np

    def extract(path):
        arr = np.load(path)
        return [arr.mean(), arr.std(), np.abs(arr).max()]

    ids = sorted(f[:-4] for f in os.listdir("train") if f.endswith(".npy"))
    feats = []
    for fid in ids:
        feats.append(extract(os.path.join("train", fid + ".npy")))
    X = np.array(feats)

    Feature extraction over ~50k files runs on one core: wall-clock time
    scales up by roughly the available-core factor (e.g. ~1437s vs ~109s,
    >10x slower), leaving less budget for feature engineering or model
    tuning within the same run and yielding a weaker final metric.

    import os, numpy as np
    from concurrent.futures import ProcessPoolExecutor

    def extract(path):
        arr = np.load(path)
        return [arr.mean(), arr.std(), np.abs(arr).max()]

    ids = sorted(f[:-4] for f in os.listdir("train") if f.endswith(".npy"))
    paths = [os.path.join("train", fid + ".npy") for fid in ids]
    with ProcessPoolExecutor(max_workers=os.cpu_count()) as ex:
        X = np.array(list(ex.map(extract, paths, chunksize=64)))

P1 — Sequential per-file feature extraction with per-file directory walk fallback

    feats = []
    for fid in df["id"]:
        path = os.path.join(DATA_DIR, f"{fid}.npy")
        if not os.path.exists(path):
            for root, _, files in os.walk(DATA_DIR):
                if f"{fid}.npy" in files:
                    path = os.path.join(root, f"{fid}.npy")
                    break
        arr = np.load(path)
        feats.append(extract_stats(arr))
    X = np.array(feats)

    Feature extraction runs single-core and re-walks the entire directory tree
    for every file whose flat path misses; wall-clock is roughly an order of
    magnitude higher than a mapped/parallel equivalent (observed ~1243s vs
    ~67s), leaving no time budget for richer features or more model tuning.

    path_map = {os.path.basename(p): p
                for p in glob.glob(os.path.join(DATA_DIR, "**", "*.npy"),
                                   recursive=True)}
    def load_one(fid):
        return extract_stats(np.load(path_map[f"{fid}.npy"]))

    with ProcessPoolExecutor() as ex:
        feats = list(ex.map(load_one, df["id"]))
    X = np.array(feats)

P1 — Unordered set intersection determines feature column order

    common_cols = list(set(X.columns) & set(X_test.columns))
    X = X[common_cols]
    X_test = X_test[common_cols]

    model = SomeTreeEnsemble(n_estimators=100, random_state=42)
    model.fit(X, y)
    preds = model.predict(X_test)
    pd.DataFrame({'Id': ids, 'LABEL_A': preds}).to_csv('out.csv', index=False)

    Feature column order changes between interpreter invocations due to hash
    randomization, so the fitted model and its predictions differ run to run
    despite random_state=42; the evaluation metric fluctuates across
    identical reruns (observed gap up to ~0.02 versus a deterministic
    ordering), making the reported score irreproducible.

    common_cols = [c for c in X.columns if c in set(X_test.columns)]
    X = X[common_cols]
    X_test = X_test[common_cols]

    model = SomeTreeEnsemble(n_estimators=100, random_state=42)
    model.fit(X, y)
    preds = model.predict(X_test)

P1 — Feature list from unordered set intersection

    import pandas as pd
    from sklearn.ensemble import RandomForestClassifier

    X = train.drop(columns=["target"])
    X_test = test.copy()
    common_cols = list(set(X.columns) & set(X_test.columns))
    X = X[common_cols]
    X_test = X_test[common_cols]
    model = RandomForestClassifier(random_state=42)
    model.fit(X, train["target"])
    preds = model.predict(X_test)

    Feature column order differs between reruns of the same script (set
    iteration follows per-process string hash randomization), changing
    tree-split tie-breaking; the reported metric drifts run-to-run
    (observed delta ~0.023) even though random_state is fixed, so the
    result cannot be reproduced.

    import pandas as pd
    from sklearn.ensemble import RandomForestClassifier

    X = train.drop(columns=["target"])
    X_test = test.copy()
    common_cols = [c for c in X.columns if c in set(X_test.columns)]
    X = X[common_cols]
    X_test = X_test[common_cols]
    model = RandomForestClassifier(random_state=42)
    model.fit(X, train["target"])
    preds = model.predict(X_test)

P1 — Label-feature alignment by prefix truncation after filtering

    feats = []
    cats = []
    for rec in iterate_records("train.bin"):
        feats.append(extract(rec))
        cats.append(rec.get("label"))
    X = np.array(feats)
    y = enc.fit_transform([c for c in cats if c is not None])
    X = X[:len(y)]
    model.fit(X, y)

    Any record with a missing label that is not at the tail shifts all
    subsequent labels relative to their feature rows; training accuracy
    and held-out metric drop toward chance level (observed -0.33 on the
    evaluation metric versus correctly paired data), with no error raised.

    feats = []
    cats = []
    for rec in iterate_records("train.bin"):
        c = rec.get("label")
        if c is None:
            continue
        feats.append(extract(rec))
        cats.append(c)
    X = np.array(feats)
    y = enc.fit_transform(cats)
    model.fit(X, y)

P1 — Pose lookup by sample id without sensor/key-frame filter

    sd = json.load(open("sample_data.json"))
    poses = {p["token"]: p for p in json.load(open("ego_pose.json"))}
    sample_to_pose = {}
    for rec in sd:
        tok = rec["sample_token"]
        if tok not in sample_to_pose:
            sample_to_pose[tok] = poses[rec["ego_pose_token"]]
    for tok in df["Id"]:
        ego = sample_to_pose.get(tok)
        tx, ty, tz = ego["translation"]
        rows.append(f"{tx + dx:.3f} {ty + dy:.3f} {tz:.3f}")

    Each sample's pose comes from whichever sensor record happens first in the
    table, often a non-key-frame camera sweep captured tens of milliseconds
    away from the annotated timestamp. Predicted boxes are systematically
    offset by the ego displacement over that interval (meters at driving
    speed), so overlap with ground truth collapses at tight IoU thresholds
    and the mean-AP metric drops sharply relative to a filtered pose join.

    SENSOR_A = "sensor_a"
    sd = json.load(open("sample_data.json"))
    poses = {p["token"]: p for p in json.load(open("ego_pose.json"))}
    sample_to_pose = {}
    for rec in sd:
        if not rec["is_key_frame"] or rec["channel"] != SENSOR_A:
            continue
        sample_to_pose[rec["sample_token"]] = poses[rec["ego_pose_token"]]
    for tok in df["Id"]:
        ego = sample_to_pose.get(tok)

P1 — Cross-table lookup keyed on wrong identifier

    anns = json.load(open('table_b.json'))
    samples = json.load(open('table_a.json'))
    lookup = {a['token']: a for a in anns}   # keyed on child's own id
    rows = []
    for s in samples:
        if s['token'] in lookup:             # probes with parent's id
            rows.append((s['token'], lookup[s['token']]['value']))
    print(len(rows))

    len(rows) is 0 on every run: the assembled training set is empty, the
    pipeline takes its no-data fallback and emits only blank placeholder
    outputs, and the evaluation metric lands at the trivial baseline (0.0)
    instead of the positive score a correctly keyed join produces.

    anns = json.load(open('table_b.json'))
    samples = json.load(open('table_a.json'))
    lookup = {}
    for a in anns:
        lookup.setdefault(a['sample_token'], []).append(a)  # foreign key
    rows = []
    for s in samples:
        for a in lookup.get(s['token'], []):
            rows.append((s['token'], a['value']))
    assert len(rows) > 0, "join produced no matches"

P1 — Stop-word removal or small vocabulary cap on a stylometric text task

    from sklearn.feature_extraction.text import TfidfVectorizer
    from sklearn.linear_model import LogisticRegression

    # predict which author wrote each sentence
    vec = TfidfVectorizer(stop_words='english', max_features=5000)
    X_train = vec.fit_transform(train['text'])
    X_test = vec.transform(test['text'])

    model = LogisticRegression(max_iter=1000)
    model.fit(X_train, train['author_label'])
    probs = model.predict_proba(X_test)

    Multi-class log loss substantially worse (~0.4 higher, tens of percent
    relative degradation) than the same pipeline with stop words retained and
    no vocabulary cap, because per-author function-word usage rates — the
    strongest stylistic discriminator — are absent from the feature matrix.

    from sklearn.feature_extraction.text import TfidfVectorizer
    from sklearn.linear_model import LogisticRegression

    # predict which author wrote each sentence
    vec = TfidfVectorizer(ngram_range=(1, 2), sublinear_tf=True)
    X_train = vec.fit_transform(train['text'])
    X_test = vec.transform(test['text'])

    model = LogisticRegression(max_iter=1000)
    model.fit(X_train, train['author_label'])
    probs = model.predict_proba(X_test)

P1 — Fixed central-fraction slice window in volumetric feature extraction

    def subject_features(subject_dir):
        files = sorted(os.listdir(subject_dir))
        n = len(files)
        lo, hi = int(n * 0.3), int(n * 0.7)
        feats = []
        for f in files[lo:hi]:
            img = load_slice(os.path.join(subject_dir, f))
            feats.append([img.mean(), img.std(), (img > 0).mean()])
        return np.array(feats).mean(axis=0)

    Subjects whose target structure lies in the first 30% or last 30% of the
    slice stack yield features indistinguishable from background; ranking
    metric (AUC) drops by ~0.17 versus sampling across the full slice range.

    def subject_features(subject_dir):
        files = sorted(os.listdir(subject_dir))
        n = len(files)
        idx = np.linspace(0, n - 1, 16).astype(int)
        feats = []
        for i in idx:
            img = load_slice(os.path.join(subject_dir, files[i]))
            feats.append([img.mean(), img.std(), (img > 0).mean()])
        return np.array(feats).mean(axis=0)

P1 — Word-only text features where sub-word style carries signal

    vec = TfidfVectorizer(ngram_range=(1, 2), min_df=2)
    X_tr = vec.fit_transform(train_df["text"])
    X_te = vec.transform(test_df["text"])

    model = LogisticRegression(max_iter=1000)
    model.fit(X_tr, train_df["label"])
    proba = model.predict_proba(X_te)

    Multiclass log loss is ~0.4 higher than the same classifier fed the
    word block concatenated with a character n-gram TF-IDF block; the
    orthographic/stylistic signal in the text is systematically discarded,
    so probability estimates are less confident and less accurate.

    word_vec = TfidfVectorizer(ngram_range=(1, 2), min_df=2)
    char_vec = TfidfVectorizer(analyzer="char_wb", ngram_range=(2, 4), min_df=3)
    Xw_tr = word_vec.fit_transform(train_df["text"])
    Xc_tr = char_vec.fit_transform(train_df["text"])
    X_tr = hstack([Xw_tr, Xc_tr]).tocsr()

    model = LogisticRegression(C=4.0, max_iter=1000)
    model.fit(X_tr, train_df["label"])

P1 — Prefix slice of flattened image pixels as feature vector

    features = []
    for path in image_paths:
        img = Image.open(path).convert('L').resize((64, 64))
        arr = np.array(img, dtype=np.float32) / 255.0
        features.append(arr.flatten()[:512])
    X = np.vstack(features)
    scaler = StandardScaler()
    X_scaled = scaler.fit_transform(X)
    model.fit(X_scaled, y)

    Only 512 of 4096 pixels (the top 8 rows of each 64x64 image) reach the
    model; the bottom 87% of every image is discarded. Validation log loss
    is ~20% higher than the same model trained on a spatially complete
    feature representation of identical dimensionality.

    features = []
    for path in image_paths:
        img = Image.open(path).convert('L').resize((16, 16))
        arr = np.array(img, dtype=np.float32) / 255.0
        features.append(arr.flatten())
    X = np.vstack(features)
    scaler = StandardScaler()
    X_scaled = scaler.fit_transform(X)
    model.fit(X_scaled, y)

P1 — Global intensity statistics computed over zero-valued background pixels

    def extract_features(path):
        arr = pydicom.dcmread(path).pixel_array.astype(np.float32)
        feats = [
            np.mean(arr),
            np.std(arr),
            np.percentile(arr, 25),
            np.percentile(arr, 75),
            skew(arr.flatten()),
        ]
        return feats

    Classification AUC drops ~10% relative versus foreground-masked features:
    per-subject feature values vary with the fraction of zero background
    (framing/crop) instead of the foreground intensity distribution, so the
    classifier learns background area rather than the discriminative signal.

    def extract_features(path):
        arr = pydicom.dcmread(path).pixel_array.astype(np.float32)
        fg = arr[arr > 0]
        if fg.size == 0:
            return [0.0] * 6
        feats = [
            np.mean(fg),
            np.std(fg),
            np.percentile(fg, 25),
            np.percentile(fg, 75),
            skew(fg),
            fg.size / arr.size,  # background fraction as its own feature
        ]
        return feats

P1 — Single-mean-plus-noise spatial prior instead of multi-mode extraction

    stats = train.groupby("cls")[["x", "y", "z"]].agg(["mean", "std"])
    rng = np.random.default_rng(0)
    preds = []
    for sample_id in test_ids:
        for c in classes:
            mx, my, mz = stats.loc[c, [("x","mean"),("y","mean"),("z","mean")]]
            px = mx + rng.normal(0, stats.loc[c, ("x","std")] * 0.5)
            py = my + rng.normal(0, stats.loc[c, ("y","std")] * 0.5)
            preds.append((sample_id, c, px, py, mz))

    Predicted locations scatter around a single low-density centroid between the
    true density modes; overlap-based precision drops toward zero (observed gap
    ~0.88 on the evaluation metric versus a mode-seeking baseline using the same
    training statistics).

    from sklearn.cluster import KMeans
    km = KMeans(n_clusters=8, random_state=0).fit(train[["x", "y"]])
    cands = [(cx, cy, n) for (cx, cy), n in
             zip(km.cluster_centers_, np.bincount(km.labels_))]
    preds = []
    for sample_id in test_ids:
        for cx, cy, n in cands:
            preds.append((sample_id, cx, cy, n / len(train)))

P1 — Hardcoded spatial offsets ignoring available training coordinates

    offsets = [(16.0, 0.0), (8.0, 3.0), (8.0, -3.0), (-8.0, 0.0)]
    rows = []
    for sid in sub_df["Id"]:
        ego = poses[sid]
        tx, ty, tz = ego["translation"]
        yaw = ego["yaw"]
        parts = []
        for dx, dy in offsets:
            wx = tx + dx * math.cos(yaw) - dy * math.sin(yaw)
            wy = ty + dx * math.sin(yaw) + dy * math.cos(yaw)
            parts.append(f"1.0 {wx:.3f} {wy:.3f} {tz:.3f} {W} {L} {H} {yaw:.4f} LABEL_A")
        rows.append(" ".join(parts))

    Predicted positions cluster at fixed guessed offsets that rarely overlap the
    empirical object-location distribution; IoU with ground truth falls below
    the matching threshold for nearly all predictions, driving the overlap-
    thresholded metric to ~0 versus a data-fitted placement baseline.

    ann = pd.read_csv("train.csv")
    stats = ann.groupby("label")[["rel_x", "rel_y", "rel_z"]].agg(["mean", "std"])
    rows = []
    for sid in sub_df["Id"]:
        ego = poses[sid]
        m = stats.loc["LABEL_A"]
        rx = m[("rel_x", "mean")] + rng.normal(0, m[("rel_x", "std")] * 0.5)
        ry = m[("rel_y", "mean")] + rng.normal(0, m[("rel_y", "std")] * 0.5)
        center = ego["trans"] + ego["R"].dot([rx, ry, m[("rel_z", "mean")]])
        rows.append(f"1.0 {center[0]:.3f} {center[1]:.3f} {center[2]:.3f}")

P1 — Stop-word removal in an authorship/style text pipeline

    from sklearn.feature_extraction.text import TfidfVectorizer
    from sklearn.linear_model import LogisticRegression

    # target: which author wrote each sentence
    vec = TfidfVectorizer(stop_words='english', max_features=20000)
    X_train = vec.fit_transform(train['text'])
    X_test = vec.transform(test['text'])

    model = LogisticRegression(max_iter=1000)
    model.fit(X_train, train['author'])
    probs = model.predict_proba(X_test)

    Multiclass log loss on the authorship task worsens substantially
    (observed ~0.40 absolute increase versus an identical pipeline that
    keeps stop words), because function-word frequencies — the strongest
    per-author discriminators — are removed before the model sees them.

    from sklearn.feature_extraction.text import TfidfVectorizer
    from sklearn.linear_model import LogisticRegression

    # target: which author wrote each sentence
    vec = TfidfVectorizer(ngram_range=(1, 2), max_features=20000)
    X_train = vec.fit_transform(train['text'])
    X_test = vec.transform(test['text'])

    model = LogisticRegression(max_iter=1000)
    model.fit(X_train, train['author'])
    probs = model.predict_proba(X_test)

P1 — Capped word-only TF-IDF with no character n-gram features

    from sklearn.feature_extraction.text import TfidfVectorizer
    from sklearn.linear_model import LogisticRegression

    vec = TfidfVectorizer(max_features=5000, stop_words='english')
    X_train = vec.fit_transform(train['text'])
    X_test = vec.transform(test['text'])

    model = LogisticRegression(max_iter=1000)
    model.fit(X_train, y_train)
    probs = model.predict_proba(X_test)

    Multiclass log loss is substantially higher than with an uncapped
    word + character n-gram representation (observed ~0.60 vs ~0.35,
    about 42% relative degradation) on an otherwise identical pipeline.

    from sklearn.feature_extraction.text import TfidfVectorizer
    from scipy.sparse import hstack
    from sklearn.linear_model import LogisticRegression

    word_vec = TfidfVectorizer(ngram_range=(1, 2), min_df=2)
    char_vec = TfidfVectorizer(analyzer='char_wb', ngram_range=(2, 5), max_features=50000)
    X_train = hstack([word_vec.fit_transform(train['text']),
                      char_vec.fit_transform(train['text'])]).tocsr()
    model = LogisticRegression(max_iter=1000)
    model.fit(X_train, y_train)

P1 — Word-token-only features for style-based text classification

    tfidf = TfidfVectorizer(max_features=5000, ngram_range=(1, 2), min_df=2)
    X_tr = tfidf.fit_transform(train_df["text"])
    X_te = tfidf.transform(test_df["text"])

    model = LogisticRegression(max_iter=1000)
    model.fit(X_tr, y_train)
    proba = model.predict_proba(X_te)
    pd.DataFrame(proba, columns=classes).to_csv("submission.csv", index=False)

    Log loss on the held-out evaluation is ~0.44 higher (worse) than the same
    classifier fed word-plus-character-n-gram features; sub-word stylistic
    cues carried by punctuation and morphology never enter the model.

    word_vec = TfidfVectorizer(ngram_range=(1, 2), min_df=2)
    char_vec = TfidfVectorizer(analyzer="char_wb", ngram_range=(2, 5))
    X_tr = hstack([word_vec.fit_transform(train_df["text"]),
                   char_vec.fit_transform(train_df["text"])])
    X_te = hstack([word_vec.transform(test_df["text"]),
                   char_vec.transform(test_df["text"])])
    model = LogisticRegression(max_iter=2000)
    model.fit(X_tr, y_train)

P1 — Pixel-domain aggregates for compression-domain perturbation detection

    def extract_features(path):
        img = np.array(Image.open(path).convert('L'), dtype=np.float32)
        gx = np.abs(np.diff(img, axis=1)).mean()
        gy = np.abs(np.diff(img, axis=0)).mean()
        hist, _ = np.histogram(img, bins=16, range=(0, 255))
        return [img.mean(), img.std(), gx, gy, *hist]

    X = np.array([extract_features(p) for p in train_paths])
    clf = RandomForestClassifier(n_estimators=200).fit(X, y)

    Held-out weighted AUC 0.582, barely above chance and ~0.09 absolute
    (~13% relative) below a baseline using quantized transform-coefficient
    histograms on the same data; ranking of positives is systematically
    poor because the embedding signal is absent from pixel aggregates.

    def extract_features(path):
        im = Image.open(path)
        qt = np.array(im.quantization[0]).reshape(8, 8)
        y = np.array(im.convert('L'), dtype=np.float32) - 128
        blocks = y[:504, :504].reshape(63, 8, 63, 8).transpose(0, 2, 1, 3)
        coefs = np.round(dctn(blocks, axes=(2, 3), norm='ortho') / qt)
        hist, _ = np.histogram(coefs, bins=np.arange(-8, 9))
        return hist.tolist()

P1 — Contiguous-prefix training subsample of a sequential data file

    X, y = [], []
    count = 0
    for record in iter_records("train_data.bin"):
        X.append(extract_features(record))
        y.append(record["label"])
        count += 1
        if count >= 50000:
            break
    model.fit(np.asarray(X), y)

    The model is fitted only on classes present in the first 50k records of a
    multi-million-record file stored in grouped order; any class whose records
    all occur later has zero recall, and overall accuracy drops sharply versus
    a strided sample of identical size (observed metric gap ~0.97).

    X, y = [], []
    step = max(1, total_records // 50000)
    for i, record in enumerate(iter_records("train_data.bin")):
        if i % step != 0:
            continue
        X.append(extract_features(record))
        y.append(record["label"])
        if len(X) >= 50000:
            break
    model.fit(np.asarray(X), y)

P1 — Axis-only reductions for faint slanted-signal detection

    def make_features(path):
        arr = np.load(path).astype(np.float32)   # shape (T, F)
        feats = []
        for panel in arr:
            feats += [panel.mean(), panel.std(),
                      np.percentile(panel, 99),
                      panel.max(axis=0).mean(),
                      panel.sum(axis=1).max(),
                      float(np.argmax(panel.mean(axis=0)))]
        return np.array(feats)

    X = np.stack([make_features(p) for p in paths])
    model.fit(X, y)

    Out-of-fold ranking metric sits near chance (AUC ~0.51) because a faint
    drifting line contributes negligibly to every global or axis-aligned
    statistic; trajectory-integrated features on the same classifier reach
    ~0.71 AUC, a ~0.28 absolute degradation with no error or warning emitted.

    def make_features(path):
        arr = np.load(path).astype(np.float32)   # shape (T, F)
        feats = [arr.mean(), arr.std()]
        T = arr.shape[0]
        for slope in np.linspace(-4, 4, 17):
            shifted = np.stack([np.roll(arr[t], int(slope * t))
                                for t in range(T)])
            profile = shifted.sum(axis=0)         # line integrals per slope
            feats += [profile.max(), profile.max() / (profile.std() + 1e-6)]
        return np.array(feats)

P1 — Grayscale collapse discards chroma signal in subtle-signal image task

    def extract_features(path):
        img = Image.open(path).convert('L')
        arr = np.asarray(img, dtype=np.float32)
        resid = arr - scipy.ndimage.median_filter(arr, size=3)
        return np.array([resid.mean(), resid.std(),
                         np.abs(resid).mean(), (resid**2).mean()])

    X = np.vstack([extract_features(p) for p in train_paths])
    model = lgb.train(params, lgb.Dataset(X, label=y))

    Model trains and predicts cleanly, but the discrimination metric
    (weighted AUC) is ~1.4 points absolute lower than the same pipeline
    computing residual statistics on each channel of a YCbCr decomposition,
    because signal embedded in the chroma planes never reaches the model.

    def extract_features(path):
        img = Image.open(path).convert('YCbCr')
        arr = np.asarray(img, dtype=np.float32)
        feats = []
        for c in range(3):
            plane = arr[..., c]
            resid = plane - scipy.ndimage.median_filter(plane, size=3)
            feats += [resid.mean(), resid.std(), np.abs(resid).mean()]
        return np.array(feats)

P1 — CV scores computed but ensemble averaged with fixed weights

    models = {"a": model_a, "b": model_b}
    preds = []
    for name, m in models.items():
        sc = cross_val_score(m, Xtr, ytr, cv=5, scoring="roc_auc")
        print(name, "cv", sc.mean())
        m.fit(Xtr, ytr)
        preds.append(m.predict_proba(Xte)[:, 1])
    p = np.mean(preds, axis=0)
    sub["LABEL_A"] = p
    sub.to_csv("submission.csv", index=False)

    When one member's CV score is materially lower than the other's (e.g. 0.55
    vs 0.72 AUC), the uniform average pulls the final test metric toward the
    weak member; observed drop of ~0.18 in the leaderboard metric versus a
    score-informed combination, with no error or warning emitted.

    scores, preds = [], []
    for name, m in models.items():
        sc = cross_val_score(m, Xtr, ytr, cv=5, scoring="roc_auc").mean()
        scores.append(sc)
        m.fit(Xtr, ytr)
        preds.append(m.predict_proba(Xte)[:, 1])
    w = np.array(scores) - 0.5
    w = np.clip(w, 0, None); w = w / w.sum()
    p = np.average(preds, axis=0, weights=w)

P1 — Hardcoded cap on rows sent through the fitted model at inference time

    MAX_ROWS = 50000
    processed = 0
    for rec in iter_records(test_path):
        rid = rec["_id"]
        if processed < MAX_ROWS:
            feat = extract_features(rec)
            if feat is not None:
                pred = model.predict([feat])[0]
                writer.writerow([rid, int(pred)])
                processed += 1
                continue
        writer.writerow([rid, int(most_common_label)])

    Every test row beyond the first 50,000 receives the constant majority label;
    on a multi-million-row test set, accuracy collapses toward the
    majority-class baseline (observed metric fell from ~0.30 to ~0.013).

    for rec in iter_records(test_path):
        rid = rec["_id"]
        feat = extract_features(rec)
        if feat is None:
            missing_ids.append(rid)
            continue
        buf_f.append(feat)
        buf_id.append(rid)
        if len(buf_f) >= 4096:
            flush_and_predict(buf_f, buf_id)
    flush_and_predict(buf_f, buf_id)
    for rid in missing_ids:
        writer.writerow([rid, fallback_cat])

P1 — Handcrafted intensity features from unstandardized per-sample arrays

    def load_features(ids, folder):
        rows = []
        for i in ids:
            arr = np.load(os.path.join(folder, i + ".npy"))
            rows.append([arr.mean(), arr.std(), arr.max(),
                         np.percentile(arr, 99),
                         arr[0].mean() - arr[1].mean()])
        return np.array(rows)

    X = load_features(train_ids, train_dir)
    X = StandardScaler().fit_transform(X)  # column-wise only
    model.fit(X, y)

    Validation ranking AUC collapses toward 0.5 (chance level); feature
    vectors are dominated by per-sample gain and noise-floor differences
    rather than the target, costing roughly 0.3 absolute on the ranking
    metric versus the same features computed after per-sample
    standardization. Runtime and exit status are unaffected.

    def load_features(ids, folder):
        rows = []
        for i in ids:
            arr = np.load(os.path.join(folder, i + ".npy"))
            arr = arr - np.median(arr, axis=-1, keepdims=True)
            arr = arr / (arr.std(axis=-1, keepdims=True) + 1e-8)
            rows.append([arr.mean(), arr.std(), arr.max(),
                         np.percentile(arr, 99),
                         arr[0].mean() - arr[1].mean()])
        return np.array(rows)

P1 — Single-scale detection statistics on spectrogram arrays

    def features(path):
        arr = np.load(path).astype(np.float32)   # (T, F) spectrogram
        arr = (arr - arr.mean()) / (arr.std() + 1e-6)
        col = arr.mean(axis=0)                   # line integral per freq bin
        feats = [col.max(), col.mean(),
                 np.percentile(col, 99),
                 arr.max(), arr.max(axis=0).std()]
        return np.array(feats, dtype=np.float32)

    X = np.stack([features(p) for p in paths])
    model.fit(X, y)

    Ranking metric (AUC) is ~10% relative lower than the same pipeline with
    duplicated statistics on a frequency-smoothed copy: signals wider than one
    frequency bin produce a peak response diluted by neighboring noise bins,
    so wide-signal positives score close to noise and are ranked below them.

    from scipy.ndimage import uniform_filter1d

    def features(path):
        arr = np.load(path).astype(np.float32)
        arr = (arr - arr.mean()) / (arr.std() + 1e-6)
        feats = []
        for k in (1, 4, 16):
            a = arr if k == 1 else uniform_filter1d(arr, size=k, axis=1)
            col = a.mean(axis=0)
            feats += [col.max(), col.mean(), np.percentile(col, 99)]
        return np.array(feats, dtype=np.float32)

P1 — Fitted results never reach the prediction path

    train = pd.read_csv("train.csv")
    stats = train.groupby("LABEL_A")["col_a"].mean().to_dict()

    rows = []
    for sample_id in sample_submission["Id"]:
        pred_string = ""  # placeholder, stats never consulted
        rows.append({"Id": sample_id, "PredictionString": pred_string})

    pd.DataFrame(rows).to_csv("submission.csv", index=False)

    Every output row is empty; the submission scores at the trivial
    no-prediction baseline (metric near 0) despite the compute spent
    building `stats`, a large drop versus using the fitted statistics.

    train = pd.read_csv("train.csv")
    stats = train.groupby("LABEL_A")["col_a"].mean().to_dict()

    rows = []
    for sample_id in sample_submission["Id"]:
        est = stats.get("LABEL_A", 0.0)
        pred_string = f"{est:.3f}"
        rows.append({"Id": sample_id, "PredictionString": pred_string})

    pd.DataFrame(rows).to_csv("submission.csv", index=False)

P1 — Prefix-only subsample of a sequentially-stored training file

    X, y = [], []
    n = 0
    with open("train.bin", "rb") as f:
        for rec in stream_records(f):
            feat = featurize(rec["payload"])
            if feat is not None:
                X.append(feat)
                y.append(rec["label"])
                n += 1
            if n >= 100000:
                break
    model.fit(np.asarray(X), np.asarray(y))

    The sample covers only the first ~2% of the file; labels stored later never
    appear in the training set, so the fitted model assigns them zero probability.
    Held-out accuracy is bounded by the label mass of the file prefix — measured
    0.956 lower on the task metric than a stride-sampled model with the same
    100k-record budget and identical features.

    X, y = [], []
    n, i = 0, 0
    stride = est_total_records // 100000
    with open("train.bin", "rb") as f:
        for rec in stream_records(f):
            i += 1
            if i % stride != 0:
                continue
            feat = featurize(rec["payload"])
            if feat is not None:
                X.append(feat); y.append(rec["label"]); n += 1
    model.fit(np.asarray(X), np.asarray(y))

P1 — Constant placeholder written for every test instance despite training computation

    train_df = pd.read_csv("train.csv")
    priors = train_df.groupby("col_a").size() / len(train_df)  # computed, never used

    sub = pd.read_csv("sample_submission.csv")
    predictions = []
    for row_id in sub["Id"]:
        if row_id in known_ids:
            pred = ""   # placeholder
        else:
            pred = ""
        predictions.append(pred)
    sub["Prediction"] = predictions
    sub.to_csv("submission.csv", index=False)

    Evaluation metric collapses to the trivial-baseline floor (0.0), strictly
    below what even a crude training-derived prior would score, because no
    learned information reaches the output file.

    train_df = pd.read_csv("train.csv")
    prior = train_df["col_a"].mean()

    sub = pd.read_csv("sample_submission.csv")
    predictions = []
    for row_id in sub["Id"]:
        if row_id in known_ids:
            predictions.append(prior)   # training-derived value
        else:
            predictions.append("")      # fallback only for unknown ids
    sub["Prediction"] = predictions
    sub.to_csv("submission.csv", index=False)

P1 — Per-group history collapsed to a single summary-statistic target

    train = pd.read_csv("train.csv")  # cols: id, week, val, col_a

    def slope(g):
        return np.polyfit(g["week"], g["val"], 1)[0]

    per_id = train.groupby("id").apply(slope).rename("slope").reset_index()
    per_id = per_id.merge(train.groupby("id").first().reset_index(), on="id")
    X = per_id[["col_a"]]
    y = per_id["slope"]
    model = LinearRegression().fit(X, y)
    pred_val = base_val + model.predict(X_test) * weeks_ahead

    Training set shrinks from ~N_measurements rows to ~N_subjects rows (a
    reduction by the average measurements-per-subject factor), and the model
    fits noisy per-subject slope estimates; forecast error on the evaluation
    metric is ~0.056 worse than regressing on all measurement rows directly.

    train = pd.read_csv("train.csv")  # cols: id, week, val, col_a
    base = train.sort_values("week").groupby("id").first().reset_index()
    base = base.rename(columns={"week": "base_week", "val": "base_val"})
    pairs = train.merge(base[["id", "base_week", "base_val"]], on="id")
    pairs["dw"] = pairs["week"] - pairs["base_week"]
    X = pairs[["base_val", "dw", "col_a"]]
    y = pairs["val"] - pairs["base_val"]
    model = Ridge().fit(X, y)

P1 — Word-tokens-only features for a stylistic-identity target

    from sklearn.feature_extraction.text import TfidfVectorizer
    from sklearn.linear_model import LogisticRegression

    # target: which author wrote each sentence
    vec = TfidfVectorizer(ngram_range=(1, 2), max_features=50000)
    X_train = vec.fit_transform(train["text"])
    X_test = vec.transform(test["text"])

    model = LogisticRegression(C=5, max_iter=1000)
    model.fit(X_train, train["author_label"])
    preds = model.predict_proba(X_test)

    Program runs cleanly, but multiclass log loss is materially higher
    (observed gap ~0.39 on the evaluation metric) than the same pipeline
    augmented with character n-gram features, because character-level
    style signal (spelling, punctuation habits, morphology) is never
    presented to the model.

    from sklearn.feature_extraction.text import TfidfVectorizer
    from scipy.sparse import hstack
    from sklearn.linear_model import LogisticRegression

    vec_w = TfidfVectorizer(ngram_range=(1, 2), max_features=50000)
    vec_c = TfidfVectorizer(analyzer="char_wb", ngram_range=(2, 5))
    X_train = hstack([vec_w.fit_transform(train["text"]),
                      vec_c.fit_transform(train["text"])]).tocsr()
    X_test = hstack([vec_w.transform(test["text"]),
                     vec_c.transform(test["text"])]).tocsr()
    model = LogisticRegression(C=5, max_iter=1000).fit(X_train, train["author_label"])

P1 — Style signal discarded by stop-word removal plus capped word-only vocabulary

    from sklearn.feature_extraction.text import TfidfVectorizer
    from sklearn.linear_model import LogisticRegression

    # classify sentences by which writer produced them
    vec = TfidfVectorizer(stop_words='english', max_features=10000)
    X_tr = vec.fit_transform(train['text'])
    X_te = vec.transform(test['text'])

    model = LogisticRegression(max_iter=1000)
    model.fit(X_tr, train['label'])
    pred = model.predict_proba(X_te)

    Multiclass log loss roughly doubles (≈0.60 vs ≈0.30) relative to a pipeline
    that keeps function words and adds character n-gram features; probability
    estimates are systematically less confident on the correct class because
    the strongest stylistic markers were removed before fitting.

    from sklearn.feature_extraction.text import TfidfVectorizer
    from scipy.sparse import hstack
    from sklearn.linear_model import LogisticRegression

    word_vec = TfidfVectorizer(min_df=2, sublinear_tf=True)
    char_vec = TfidfVectorizer(analyzer='char_wb', ngram_range=(2, 5))
    X_tr = hstack([word_vec.fit_transform(train['text']),
                   char_vec.fit_transform(train['text'])])
    model = LogisticRegression(max_iter=2000)
    model.fit(X_tr, train['label'])

P1 — Text represented only by scalar surface statistics

    from sklearn.feature_extraction.text import TfidfVectorizer  # imported, unused
    from sklearn.linear_model import LogisticRegression

    df = pd.read_csv('train.csv')
    df['len_a'] = df['col_a'].str.len()
    df['words_a'] = df['col_a'].str.split().str.len()
    df['excl_a'] = df['col_a'].str.count('!')
    X = df[['len_a', 'words_a', 'excl_a']].values

    model = LogisticRegression()
    model.fit(X, df['LABEL_A'])
    preds = model.predict_proba(pd.read_csv('test.csv').pipe(featurize))

    Run completes and writes predictions, but held-out log loss is ~4%
    relative worse than the same model with even a shallow lexical
    representation added; predictions track only text length/style, so
    examples with similar lengths but opposite content are scored alike.

    from sklearn.feature_extraction.text import TfidfVectorizer
    from scipy.sparse import hstack

    df = pd.read_csv('train.csv')
    df['len_a'] = df['col_a'].str.len()
    df['excl_a'] = df['col_a'].str.count('!')

    vec = TfidfVectorizer(max_features=5000, ngram_range=(1, 2))
    X_text = vec.fit_transform(df['col_a'])
    X = hstack([X_text, df[['len_a', 'excl_a']].values])
    model.fit(X, df['LABEL_A'])

P1 — World-frame targets regressed without pose transform

    ann = pd.read_csv('train.csv')          # world-frame centers
    X = np.stack([hog_features(load_img(t)) for t in ann['token']])
    y = ann[['center_x', 'center_y', 'center_z']].values
    model = RandomForestRegressor().fit(X, y)

    test_X = np.stack([hog_features(load_img(t)) for t in test_tokens])
    preds = model.predict(test_X)            # written as-is, no pose used
    rows = [{'Id': t, 'Pred': fmt(p)} for t, p in zip(test_tokens, preds)]
    pd.DataFrame(rows).to_csv('submission.csv', index=False)

    Predicted world positions cluster around the training-set mean location and
    are uncorrelated with true object positions, because vehicle pose — the
    dominant determinant of world coordinates — is absent from the features.
    Overlap-based average precision stays near 0.0 versus a pose-aware baseline.

    ann = pd.read_csv('train.csv').merge(ego_pose, on='token')
    # express targets in ego-relative frame
    dx = ann['center_x'] - ann['ego_x']; dy = ann['center_y'] - ann['ego_y']
    c, s = np.cos(-ann['yaw']), np.sin(-ann['yaw'])
    y = np.stack([c*dx - s*dy, s*dx + c*dy, ann['center_z'] - ann['ego_z']], 1)
    model = RandomForestRegressor().fit(X, y)

    rel = model.predict(test_X)
    c, s = np.cos(test_yaw), np.sin(test_yaw)
    wx = test_ex + c*rel[:,0] - s*rel[:,1]
    wy = test_ey + s*rel[:,0] + c*rel[:,1]

P1 — Word-only features with stop words stripped for style attribution

    from sklearn.feature_extraction.text import TfidfVectorizer
    from sklearn.linear_model import LogisticRegression

    vec = TfidfVectorizer(stop_words='english', ngram_range=(1, 2))
    X_train = vec.fit_transform(train['text'])
    X_test = vec.transform(test['text'])

    model = LogisticRegression(max_iter=1000)
    model.fit(X_train, train['author'])
    proba = model.predict_proba(X_test)

    Multiclass log-loss is substantially higher (observed ~0.38 worse) than
    the same classifier fed word features stacked with character n-grams;
    function-word and punctuation signal that separates authors is discarded.

    from sklearn.feature_extraction.text import TfidfVectorizer
    from scipy.sparse import hstack
    from sklearn.linear_model import LogisticRegression

    word_vec = TfidfVectorizer(stop_words='english', ngram_range=(1, 2))
    char_vec = TfidfVectorizer(analyzer='char', ngram_range=(2, 5))
    X_train = hstack([word_vec.fit_transform(train['text']),
                      char_vec.fit_transform(train['text'])])
    X_test = hstack([word_vec.transform(test['text']),
                     char_vec.transform(test['text'])])
    LogisticRegression(max_iter=1000).fit(X_train, train['author'])

P1 — Contiguous middle-block slice sampling in stack feature extraction

    def extract_features(case_dir):
        files = sorted(os.listdir(case_dir))
        mid = len(files) // 2
        chosen = files[mid - 10 : mid + 10]
        stats = []
        for f in chosen:
            img = load_slice(os.path.join(case_dir, f))
            stats.append([img.mean(), img.std(), img.max()])
        return np.array(stats).mean(axis=0)

    Features encode only the central ~20 slices; samples whose informative
    regions lie near the top or bottom of the stack produce feature vectors
    indistinguishable from background, lowering the classification metric by
    roughly 10% relative to full-extent strided sampling.

    def extract_features(case_dir):
        files = sorted(os.listdir(case_dir))
        idx = np.linspace(0, len(files) - 1, 20).astype(int)
        stats = []
        for i in idx:
            img = load_slice(os.path.join(case_dir, files[i]))
            stats.append([img.mean(), img.std(), img.max()])
        return np.array(stats).mean(axis=0)

P1 — Per-sample min-max rescaling before intensity-statistic features

    feats = []
    for path in image_paths:
        img = load_pixels(path).astype(np.float32)
        img = (img - img.min()) / (img.max() - img.min() + 1e-6)
        feats.append([
            img.mean(),
            img.std(),
            np.percentile(img, 90),
            (img > 0.5).mean(),
        ])
    X = np.array(feats)
    model.fit(X, y)

    Held-out ranking metric collapses toward chance (AUC ~0.50 vs ~0.65 for the
    identical pipeline computing the same statistics on raw pixel values):
    every sample is forced onto the same [0,1] intensity scale, so between-sample
    absolute-brightness differences — the only signal these global statistics
    carry — are removed before the model ever sees them.

    feats = []
    for path in image_paths:
        img = load_pixels(path).astype(np.float32)
        feats.append([
            img.mean(),
            img.std(),
            np.percentile(img, 90),
            (img > img.mean()).mean(),
        ])
    X = np.array(feats)
    scaler = StandardScaler().fit(X[train_idx])
    model.fit(scaler.transform(X[train_idx]), y[train_idx])

P1 — Small fixed vocabulary cap on sparse text features fed to sparse-capable models

    from sklearn.feature_extraction.text import TfidfVectorizer
    from sklearn.linear_model import LogisticRegression
    from sklearn.naive_bayes import MultinomialNB

    vec = TfidfVectorizer(max_features=5000, ngram_range=(1, 2))
    X_train = vec.fit_transform(df_train['col_a'])
    X_test = vec.transform(df_test['col_a'])
    lr = LogisticRegression(max_iter=1000).fit(X_train, y)
    nb = MultinomialNB().fit(X_train, y)
    pred = (lr.predict_proba(X_test) + nb.predict_proba(X_test)) / 2

    Validation log loss rises materially (observed ~0.4 absolute worsening,
    ~42% relative) versus the same pipeline with no vocabulary cap: the
    truncated vocabulary silently discards infrequent but highly
    discriminative n-grams the linear models could have exploited.

    from sklearn.feature_extraction.text import TfidfVectorizer
    from sklearn.linear_model import LogisticRegression

    vec = TfidfVectorizer(ngram_range=(1, 2), min_df=2, max_df=0.9)
    X_train = vec.fit_transform(df_train['col_a'])
    X_test = vec.transform(df_test['col_a'])
    lr = LogisticRegression(max_iter=1000).fit(X_train, y)
    pred = lr.predict_proba(X_test)

P1 — Axis-aligned-only features for a drifting-line detection target

    def extract_features(arr):  # arr: 2D array, target line drifts across columns over rows
        feats = []
        feats.append(arr.mean())
        feats.append(arr.std())
        feats.extend(arr.mean(axis=0))          # per-column average
        feats.extend(arr.max(axis=0))           # per-column peak
        gy, gx = np.gradient(arr)
        feats.append(np.abs(gx).mean())
        feats.append(np.abs(gy).mean())
        return np.array(feats)

    X = np.stack([extract_features(np.load(p)) for p in paths])

    Energy of the drifting line is smeared across many columns instead of
    accumulated along its trajectory, so faint targets stay at noise level in
    every feature; the ranking metric (AUC) lands ~0.23 below an otherwise
    identical pipeline whose features integrate along candidate drift slopes.

    def extract_features(arr, rates=range(-8, 9)):
        feats = [arr.mean(), arr.std()]
        n_rows = arr.shape[0]
        for r in rates:
            shifted = np.stack([np.roll(arr[i], -r * i) for i in range(n_rows)])
            profile = shifted.sum(axis=0)       # integrate along candidate slope
            feats.append(profile.max() / (profile.std() + 1e-9))
        return np.array(feats)

P1 — Stop-word removal in authorship-style classification

    from sklearn.feature_extraction.text import TfidfVectorizer
    from sklearn.linear_model import LogisticRegression

    vectorizer = TfidfVectorizer(max_features=50000,
                                 stop_words='english',
                                 ngram_range=(1, 2))
    X_tr = vectorizer.fit_transform(train['text'])
    X_te = vectorizer.transform(test['text'])
    model = LogisticRegression(max_iter=1000)
    model.fit(X_tr, train['author'])   # target: who wrote it
    proba = model.predict_proba(X_te)

    Multi-class log loss increases substantially (observed ~0.34-0.40 absolute,
    ~34% relative worse) because high-frequency function words — the strongest
    per-author style markers — are excluded from the feature space, so the
    classifier's predicted probabilities are systematically less confident and
    less accurate on every fold.

    from sklearn.feature_extraction.text import TfidfVectorizer
    from sklearn.linear_model import LogisticRegression

    vectorizer = TfidfVectorizer(max_features=50000,
                                 stop_words='english',
                                 ngram_range=(1, 2))
    X_tr = vectorizer.fit_transform(train['text'])
    X_te = vectorizer.transform(test['text'])
    model = LogisticRegression(max_iter=1000)
    model.fit(X_tr, train['topic'])   # target: subject category
    proba = model.predict_proba(X_te)

P1 — Word-only features for style attribution

    from sklearn.feature_extraction.text import TfidfVectorizer
    from sklearn.linear_model import LogisticRegression

    vec = TfidfVectorizer(max_features=20000, ngram_range=(1, 2))
    X_train = vec.fit_transform(train['text'])
    X_test = vec.transform(test['text'])

    model = LogisticRegression(max_iter=1000)
    model.fit(X_train, y_train)
    probs = model.predict_proba(X_test)

    Held-out multiclass log loss is substantially higher (observed ~0.42
    worse) than the same pipeline augmented with character n-gram features,
    because sub-word stylistic cues (punctuation, spelling, morphology)
    never enter the feature matrix.

    from sklearn.feature_extraction.text import TfidfVectorizer
    from scipy.sparse import hstack
    from sklearn.linear_model import LogisticRegression

    word_vec = TfidfVectorizer(ngram_range=(1, 2))
    char_vec = TfidfVectorizer(analyzer='char_wb', ngram_range=(2, 5))
    X_train = hstack([word_vec.fit_transform(train['text']),
                      char_vec.fit_transform(train['text'])]).tocsr()
    model = LogisticRegression(max_iter=1000)
    model.fit(X_train, y_train)

P1 — Global-scale division after per-channel baseline in peak-feature normalization

    def features(x):  # x: (channels, time)
        x = x - np.median(x, axis=1, keepdims=True)
        x = x / (x.std() + 1e-6)          # single scalar scale
        f1 = x.max()
        f2 = np.sort(x.ravel())[-5:].mean()
        return np.array([f1, f2])

    Extremum features saturate on the few channels with intrinsically high
    noise variance instead of the target signal; ranking metric (AUC) drops
    by roughly 0.10 relative to per-channel scaling on the same downstream model.

    def features(x):  # x: (channels, time)
        x = x - np.median(x, axis=1, keepdims=True)
        s = x.std(axis=1, keepdims=True) + 1e-6
        x = x / s                          # per-channel scale
        f1 = x.max()
        f2 = np.sort(x.ravel())[-5:].mean()
        return np.array([f1, f2])

P1 — Predictions from label priors only; raw per-sample inputs never read

    import pandas as pd
    train = pd.read_csv("train_labels.csv")
    meta = pd.read_csv("sample_metadata.csv")
    mean_w = train["col_w"].mean()
    mean_h = train["col_h"].mean()
    top_class = train["label"].mode()[0]
    sub = pd.read_csv("sample_submission.csv")
    sub["Prediction"] = f"1.0 {mean_w:.3f} {mean_h:.3f} {top_class}"
    sub.to_csv("submission.csv", index=False)

    Evaluation score sits at the constant-prediction baseline, orders of
    magnitude below any model that reads the per-sample sensor files;
    every sample receives the same prior-derived output regardless of
    its actual content.

    import numpy as np, pandas as pd
    train = pd.read_csv("train_labels.csv")
    mean_w = train["col_w"].mean()
    sub = pd.read_csv("sample_submission.csv")
    preds = []
    for sid in sub["Id"]:
        pts = np.fromfile(f"samples/{sid}.bin", dtype=np.float32).reshape(-1, 4)
        cx, cy = pts[:, 0].mean(), pts[:, 1].mean()
        preds.append(f"1.0 {cx:.3f} {cy:.3f} {mean_w:.3f}")
    sub["Prediction"] = preds
    sub.to_csv("submission.csv", index=False)

P1 — Prefix-truncated flattened pixel features

    def extract_features(path):
        img = Image.open(path).convert('L').resize((32, 32))
        arr = np.array(img, dtype=np.float32) / 255.0
        return arr.flatten()[:256]  # 256 of 1024 values

    X = np.vstack([extract_features(p) for p in paths])
    model.fit(X, y)

    Features encode only the top quarter of each image (row-major prefix),
    discarding 75% of the pixels; classification log loss rises ~15%
    (0.654 vs 0.552) compared to a spatially complete descriptor.

    def extract_features(path):
        img = Image.open(path).convert('L').resize((16, 16))
        arr = np.array(img, dtype=np.float32) / 255.0
        return arr.flatten()[:256]  # 16*16 = 256, full array

    X = np.vstack([extract_features(p) for p in paths])
    model.fit(X, y)

P1 — Paired-panel contrast reduced to global aggregates only

    def features(arr):                 # arr: (6, T, F); even panels on-target, odd off-target
        on = arr[0::2].mean(axis=0)
        off = arr[1::2].mean(axis=0)
        d = on - off
        return [d.mean(), d.std(), d.max(), d.min(),
                np.percentile(d, 99), np.percentile(d, 1)]

    X = np.stack([features(load_panels(i)) for i in ids])
    model.fit(X, y)

    Out-of-fold ranking metric sits near chance (~0.55-0.60 AUC) instead of
    the ~0.80+ achievable from the same arrays: a narrow signal occupying a
    few cells of a T x F difference is diluted by a factor of roughly T*F in
    every global aggregate, so signal-bearing and control examples receive
    nearly identical feature vectors.

    def features(arr):                 # arr: (6, T, F); even panels on-target, odd off-target
        on = arr[0::2].mean(axis=0)
        off = arr[1::2].mean(axis=0)
        d = uniform_filter1d(on - off, size=5, axis=0)
        r, c = np.unravel_index(np.argmax(d), d.shape)
        per_panel = np.sort(arr[:, r, c])
        return list(per_panel) + [d.max(), per_panel[-1] - per_panel[0]]

P1 — Small hardcoded vocabulary cap on sparse text features for a linear model

    from sklearn.feature_extraction.text import TfidfVectorizer
    from sklearn.linear_model import LogisticRegression

    tfidf = TfidfVectorizer(max_features=5000, ngram_range=(1, 2), min_df=2)
    X_tr = tfidf.fit_transform(train_df["col_a"])
    X_te = tfidf.transform(test_df["col_a"])

    model = LogisticRegression(max_iter=1000)
    model.fit(X_tr, y_train)
    probs = model.predict_proba(X_te)

    Multiclass log loss is substantially higher (~0.44 worse in measured
    runs) than with the full vocabulary, because rare but class-indicative
    n-grams are silently dropped from the feature space; the program still
    exits cleanly and writes predictions.

    from sklearn.feature_extraction.text import TfidfVectorizer
    from sklearn.linear_model import LogisticRegression

    tfidf = TfidfVectorizer(ngram_range=(1, 2), min_df=2, max_df=0.95)
    X_tr = tfidf.fit_transform(train_df["col_a"])
    X_te = tfidf.transform(test_df["col_a"])

    model = LogisticRegression(max_iter=1000)
    model.fit(X_tr, y_train)
    probs = model.predict_proba(X_te)

P1 — World-frame position regression ignoring available ego pose

    train = pd.read_csv('train.csv')
    X = extract_features(train)          # per-sample sensor features
    y = train[['cx', 'cy', 'cz']].values  # absolute world coordinates
    model = RandomForestRegressor().fit(X, y)

    X_test = extract_features(test)
    preds = model.predict(X_test)        # raw world-frame outputs
    for row, (px, py, pz) in zip(test.itertuples(), preds):
        out.append(f"1.0 {px:.3f} {py:.3f} {pz:.3f} {w} {l} {h} 0 LABEL_A")

    Predicted boxes are anchored near the world-coordinate range seen in
    training; as the agent traverses new regions, predictions land tens to
    hundreds of meters from ground truth, IoU with every true box is 0, and
    the IoU-thresholded mAP score drops to exactly 0.

    pose = load_pose_table('pose.json')   # per-sample translation + rotation
    rel = []
    for row in train.itertuples():
        p = pose[row.sample_id]
        rel.append(p['R'].T.dot(np.array([row.cx, row.cy, row.cz]) - p['t']))
    model = RandomForestRegressor().fit(X, np.array(rel))

    for row, r in zip(test.itertuples(), model.predict(X_test)):
        p = pose[row.sample_id]
        world = p['t'] + p['R'].dot(r)
        out.append(f"1.0 {world[0]:.3f} {world[1]:.3f} {world[2]:.3f} ...")

P1 — Single-slice feature extraction from multi-file image series

    def extract_features(case_id):
        case_dir = os.path.join(DATA_DIR, case_id)
        files = sorted(os.listdir(case_dir))
        ds = pydicom.dcmread(os.path.join(case_dir, files[0]))
        img = ds.pixel_array.astype(np.float32)
        return np.array([img.mean(), img.std(), img.max(), img.min()])

    X = np.vstack([extract_features(c) for c in case_ids])
    model.fit(X, y)

    Each case is represented by an edge slice that mostly contains background;
    the interior slices holding the discriminative signal never enter the
    feature matrix. Ranking metric (AUC) falls ~8% relative versus sampling
    multiple central slices, approaching chance-level performance.

    def extract_features(case_id):
        case_dir = os.path.join(DATA_DIR, case_id)
        files = sorted(os.listdir(case_dir), key=lambda f: int(f.split('-')[-1].split('.')[0]))
        mid = len(files) // 2
        feats = []
        for f in files[mid - 3: mid + 4]:
            img = pydicom.dcmread(os.path.join(case_dir, f)).pixel_array.astype(np.float32)
            feats.append([img.mean(), img.std(), img.max(), img.min()])
        feats = np.array(feats)
        return np.concatenate([feats.mean(0), feats.std(0)])

P1 — Constant empty prediction written for every test row

    stats = compute_class_stats(train_df)  # data-derived, computed but unused below

    predictions = []
    for sample_id in sample_submission['Id'].tolist():
        if sample_id in test_index:
            pred_string = ""
        else:
            pred_string = ""
        predictions.append({'Id': sample_id, 'PredictionString': pred_string})

    pd.DataFrame(predictions).to_csv(OUTPUT_PATH, index=False)

    Evaluation score collapses to the empty-submission baseline (metric = 0.0,
    recall exactly 0), versus a strictly positive score for any submission whose
    prediction strings are constructed from the computed statistics.

    stats = compute_class_stats(train_df)

    predictions = []
    for sample_id in sample_submission['Id'].tolist():
        if sample_id in test_index:
            pred_string = build_prediction(stats, test_index[sample_id])
        else:
            pred_string = ""
        predictions.append({'Id': sample_id, 'PredictionString': pred_string})

    pd.DataFrame(predictions).to_csv(OUTPUT_PATH, index=False)

P1 — Word-only text features for style-sensitive probabilistic classification

    vectorizer = TfidfVectorizer(max_df=0.95, lowercase=True,
                                 stop_words='english')
    X_tr = vectorizer.fit_transform(train["text"])
    X_te = vectorizer.transform(test["text"])

    model = LogisticRegression(max_iter=1000)
    model.fit(X_tr, y)
    proba = model.predict_proba(X_te)
    sub = pd.DataFrame(proba, columns=classes)
    sub.insert(0, "id", test["id"].values)
    sub.to_csv("submission.csv", index=False)

    Log loss on held-out data is substantially higher (worse) than a pipeline
    that also stacks character n-gram TF-IDF features; observed gap of ~0.40
    on the same data because sub-word stylistic signal (affixes, punctuation
    patterns) is never presented to the model.

    word_vec = TfidfVectorizer(sublinear_tf=True)
    char_vec = TfidfVectorizer(analyzer="char_wb", ngram_range=(2, 4),
                               min_df=3)
    Xw_tr = word_vec.fit_transform(train["text"])
    Xc_tr = char_vec.fit_transform(train["text"])
    X_tr = hstack([Xw_tr, Xc_tr]).tocsr()
    model = LogisticRegression(C=4.0, max_iter=1000)
    model.fit(X_tr, y)
    proba = model.predict_proba(hstack([word_vec.transform(test["text"]),
                                        char_vec.transform(test["text"])]))

P1 — Absolute-value regression ignores known per-subject baseline anchor

    X = train[["age", "week", "col_a"]].values
    y = train["target"].values
    model = Ridge().fit(X, y)

    test["target_pred"] = model.predict(test[["age", "week", "col_a"]].values)
    sub = test[["row_id", "target_pred"]].rename(columns={"target_pred": "target"})
    sub["target"] = sub["target"].round(1)
    sub.to_csv("submission.csv", index=False)

    Predictions at and near each subject's baseline week deviate from the
    exactly known measurement by the model's pooled residual error, adding
    avoidable error the metric penalizes; anchoring forecasts to the given
    baseline improved the competition metric by ~0.025.

    base = train.groupby("subject").first()[["week", "target"]]
    train = train.join(base, on="subject", rsuffix="_base")
    train["slope"] = (train["target"] - train["target_base"]) / \
                     (train["week"] - train["week_base"]).replace(0, 1)
    model = Ridge().fit(train[["age", "col_a"]].values, train["slope"].values)

    tb = test.set_index("subject")
    dw = weeks - tb.loc[subjects, "week_base"].values
    pred = tb.loc[subjects, "target_base"].values + model.predict(Xt) * dw

P1 — Global-only color histograms without spatially localized color statistics

    def extract_features(path):
        img = np.asarray(Image.open(path).convert("RGB").resize((96, 96)))
        gray = rgb2gray(img)
        tex = hog(gray, pixels_per_cell=(12, 12), cells_per_block=(2, 2))
        hists = []
        for c in range(3):
            h, _ = np.histogram(img[:, :, c], bins=16, range=(0, 255), density=True)
            hists.append(h)
        return np.concatenate([tex] + hists).astype(np.float32)

    Classification log loss is ~4% relative higher than an identical pipeline
    that additionally includes per-grid-cell channel means; ranking metric on
    held-out data degrades correspondingly because spatial color layout that
    discriminates the classes is never presented to the model.

    def extract_features(path):
        img = np.asarray(Image.open(path).convert("RGB").resize((96, 96)))
        gray = rgb2gray(img)
        tex = hog(gray, pixels_per_cell=(12, 12), cells_per_block=(2, 2))
        hists = [np.histogram(img[:, :, c], bins=16, range=(0, 255), density=True)[0]
                 for c in range(3)]
        cells = img.reshape(4, 24, 4, 24, 3).mean(axis=(1, 3)).ravel() / 255.0
        return np.concatenate([tex] + hists + [cells]).astype(np.float32)

P1 — Word-only text features for authorship/style prediction

    vec = TfidfVectorizer(max_features=20000, stop_words='english')
    X_tr = vec.fit_transform(train['text'])
    X_te = vec.transform(test['text'])

    le = LabelEncoder()
    y = le.fit_transform(train['author'])

    model = LogisticRegression(max_iter=1000)
    model.fit(X_tr, y)
    proba = model.predict_proba(X_te)

    Multi-class log loss ~0.58 instead of ~0.36 achievable with the same
    linear model when character n-gram features are stacked in — a ~38%
    relative worsening because punctuation, morphology, and function-word
    style cues are never presented to the model.

    word_vec = TfidfVectorizer(ngram_range=(1, 2))
    char_vec = TfidfVectorizer(analyzer='char', ngram_range=(2, 4))

    X_tr = hstack([word_vec.fit_transform(train['text']),
                   char_vec.fit_transform(train['text'])]).tocsr()
    X_te = hstack([word_vec.transform(test['text']),
                   char_vec.transform(test['text'])]).tocsr()

    model = LogisticRegression(C=4, max_iter=1000)
    model.fit(X_tr, train['author_id'])

P1 — Hardcoded spatial offsets instead of label-derived positions

    offsets = [(16.0, 0.0), (8.0, 3.0), (8.0, -3.0), (-8.0, 0.0)]
    for sample_id in sub_df["Id"]:
        tx, ty, tz = ego_pose[sample_id]["translation"]
        yaw = pose_yaw(ego_pose[sample_id])
        parts = []
        for dx, dy in offsets:
            wx = tx + dx * math.cos(yaw) - dy * math.sin(yaw)
            wy = ty + dx * math.sin(yaw) + dy * math.cos(yaw)
            parts.append(f"1.0 {wx:.3f} {wy:.3f} {tz:.3f} {mw} {ml} {mh} {yaw} LABEL_A")
        preds.append(" ".join(parts))

    Predicted box centers land at arbitrary fixed spots around the sensor that
    almost never overlap true objects at the required IoU thresholds; mean
    average precision drops to ~0.0 versus a baseline that places boxes at
    positions aggregated from the training annotations.

    rel = []
    for ann in train_annotations:
        rx, ry, rz = to_ego_frame(ann["translation"], ego_pose[ann["sample"]])
        rel.append([rx, ry, rz])
    centers = KMeans(n_clusters=8).fit(np.array(rel)).cluster_centers_
    for sample_id in sub_df["Id"]:
        tx, ty, tz = ego_pose[sample_id]["translation"]
        yaw = pose_yaw(ego_pose[sample_id])
        for rx, ry, rz in centers:
            wx = tx + rx * math.cos(yaw) - ry * math.sin(yaw)
            wy = ty + rx * math.sin(yaw) + ry * math.cos(yaw)
            preds_add(wx, wy, tz + rz)

P1 — Single positive variant selected per base item when multiple variant sources exist

    variant_dirs = ["method_a", "method_b", "method_c"]
    X, y = [], []
    for i, name in enumerate(base_names):
        X.append(feats(os.path.join(DATA, "originals", name)))
        y.append(0)
        alg = variant_dirs[i % len(variant_dirs)]
        X.append(feats(os.path.join(DATA, alg, name)))
        y.append(1)

    Only one third of the available positive files enter training; the model
    never sees all generation methods applied to the same content, and
    held-out weighted AUC drops by roughly 0.15-0.17 versus training on all
    provided variants.

    variant_dirs = ["method_a", "method_b", "method_c"]
    X, y, group = [], [], []
    for i, name in enumerate(base_names):
        X.append(feats(os.path.join(DATA, "originals", name)))
        y.append(0); group.append(i)
        for alg in variant_dirs:
            X.append(feats(os.path.join(DATA, alg, name)))
            y.append(1); group.append(i)

P1 — pixel-and-moment-only features for shape-dependent image classification

    def extract_features(path):
        img = Image.open(path).convert('RGB').resize((32, 32))
        arr = np.asarray(img, dtype=np.float32) / 255.0
        pixels = arr.flatten()
        means = arr.mean(axis=(0, 1))
        stds = arr.std(axis=(0, 1))
        return np.concatenate([pixels, means, stds])

    X = np.vstack([extract_features(p) for p in train_paths])
    clf = RandomForestClassifier(n_estimators=300)
    clf.fit(scaler.fit_transform(X), y)

    The classifier receives no shape or edge information, so held-out log loss
    is meaningfully higher (observed ~0.16 absolute, ~16% relative worse) than
    a same-cost pipeline that includes a gradient-orientation descriptor;
    accuracy on shape-distinguished classes drops correspondingly.

    def extract_features(path):
        img = Image.open(path).convert('RGB').resize((64, 64))
        arr = np.asarray(img, dtype=np.float32) / 255.0
        gray = rgb2gray(arr)
        hog_feats = hog(gray, orientations=9, pixels_per_cell=(8, 8))
        means = arr.mean(axis=(0, 1))
        stds = arr.std(axis=(0, 1))
        return np.concatenate([hog_feats, means, stds])

    X = np.vstack([extract_features(p) for p in train_paths])
    clf = LogisticRegression(max_iter=2000)
    clf.fit(scaler.fit_transform(X), y)

P1 — Stop-word removal or tight vocabulary cap in stylometry features

    vec = TfidfVectorizer(stop_words='english', max_features=5000)
    X_train = vec.fit_transform(train['text'])
    X_test = vec.transform(test['text'])
    model = LogisticRegression(max_iter=1000)
    model.fit(X_train, train['author_label'])
    pred = model.predict_proba(X_test)
    pd.DataFrame(pred, columns=classes).to_csv('submission.csv', index=False)

    Multiclass log loss degrades substantially (observed ~0.60 vs ~0.35 for the
    same pipeline without stop-word removal and vocabulary capping): function
    words like "the", "upon", "which" — the strongest per-author discriminators —
    are stripped from the feature space, so predicted probabilities are less
    confident and less accurate on every fold.

    vec = TfidfVectorizer(ngram_range=(1, 2), sublinear_tf=True)
    X_train = vec.fit_transform(train['text'])
    X_test = vec.transform(test['text'])
    model = LogisticRegression(max_iter=1000)
    model.fit(X_train, train['author_label'])
    pred = model.predict_proba(X_test)
    pd.DataFrame(pred, columns=classes).to_csv('submission.csv', index=False)

P1 — Interior-only slice window in volume subsampling

    def volume_features(slice_files):
        slice_files = sorted(slice_files)
        n = len(slice_files)
        lo, hi = int(n * 0.25), int(n * 0.75)
        idxs = np.linspace(lo, hi - 1, 10).astype(int)
        feats = []
        for i in idxs:
            img = load_slice(slice_files[i])
            feats.append([img.mean(), img.std(), (img > 0).mean()])
        return np.array(feats).mean(axis=0)

    Half of every subject's slices never contribute to features; per-subject
    aggregate statistics carry less discriminative signal, and the held-out
    ranking metric drops by ~0.22 versus sampling across the full index range.

    def volume_features(slice_files):
        slice_files = sorted(slice_files)
        n = len(slice_files)
        idxs = np.linspace(0, n - 1, 10).astype(int)
        feats = []
        for i in idxs:
            img = load_slice(slice_files[i])
            feats.append([img.mean(), img.std(), (img > 0).mean()])
        return np.array(feats).mean(axis=0)

P1 — Color reduced to global per-channel scalar means

    def extract_features(path):
        img = np.asarray(Image.open(path).resize((64, 64))) / 255.0
        gray = rgb2gray(img)
        shape_feat = hog(gray, pixels_per_cell=(8, 8), cells_per_block=(2, 2))
        color_feat = img.mean(axis=(0, 1))          # 3 scalars, only color info
        return np.concatenate([shape_feat, color_feat])

    X = np.vstack([extract_features(p) for p in train_paths])
    clf = LogisticRegression(max_iter=2000).fit(scaler.fit_transform(X), y)

    Validation log loss is roughly 0.10 higher than the same pipeline with
    per-channel histograms and grid-pooled channel means added; the classifier
    cannot separate classes that differ in color distribution or spatial color
    layout, so predicted probabilities are systematically less confident and
    less accurate.

    def extract_features(path):
        img = np.asarray(Image.open(path).resize((64, 64))) / 255.0
        gray = rgb2gray(img)
        shape_feat = hog(gray, pixels_per_cell=(8, 8), cells_per_block=(2, 2))
        hists = [np.histogram(img[:, :, c], bins=16, range=(0, 1))[0]
                 for c in range(3)]
        cells = img.reshape(4, 16, 4, 16, 3).mean(axis=(1, 3)).ravel()
        return np.concatenate([shape_feat, *hists, cells])

P1 — Global per-channel mean as sole color feature