scripts/inference/run_logistic_regression_ensemble.py

"""
Full ensemble script for 0109.

Loads available voter predictions for a dataset, trains LR Stacking on dev,
and evaluates on test. Supports global and per-bucket evaluation.

Expected voter sources (auto-discovered):
  - Qwen specialists: outputs/cross_domain/{dataset}/qwen7b_*_{split}_predictions.csv
  - FastDetectGPT 3B: outputs/zero_shot/{dataset}/{split}_fdgpt_scores.csv
  - Binoculars 3B: outputs/zero_shot/{dataset}/{split}_bino_scores.csv
  - BERT/RoBERTa (optional): outputs/plm/{dataset}/{name}_{split}_predictions.csv

Outputs to: outputs/ensemble_full/{dataset}/
"""
import json
import math
import numpy as np
import pandas as pd
from pathlib import Path
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import f1_score, precision_score, recall_score, accuracy_score
from scipy.special import expit

ROOT = Path(__file__).resolve().parents[2]
CROSS_DOMAIN_ROOT = ROOT / "outputs/cross_domain"
ZERO_SHOT_ROOT = ROOT / "outputs/zero_shot"
PLM_ROOT = ROOT / "outputs/plm"
OUT_ROOT = ROOT / "outputs/ensemble_full"

MIN_SAMPLES_PER_BUCKET = 20
MIN_SAMPLES_GLOBAL_LR = 50

BUCKETS = {
    "extreme_short": (0, 75),
    "short": (76, 180),
    "general": (181, 999999),
}

DS13_SUBSETS = {
    "normal": (1, 4000),
    "mixed_attack": (4001, 5000),
    "paraphrase_attack": (5001, 6000),
    "perturbation_attack": (6001, 7000),
    "len_64": (7001, 8000),
    "len_128": (8001, 9000),
    "len_256": (9001, 10000),
    "len_512": (10001, 11000),
}

VOTER_COLUMN_MAP = {
    "qwen7b_general": "pred_prob",
    "qwen7b_short": "pred_prob",
    "qwen7b_extreme_short": "pred_prob",
    "fdgpt": "fdgpt_score",
    "binoculars": "binoculars_score",
    "bert": "pred_prob",
    "roberta": "pred_prob",
}

# ---------------------------------------------------------------------------
# Helpers
# ---------------------------------------------------------------------------
def discover_voters(dataset: str):
    """Auto-discover available prediction CSVs for a dataset."""
    voters = {}

    # 1. Qwen specialists
    cd_dir = CROSS_DOMAIN_ROOT / dataset
    if cd_dir.exists():
        for split in ["dev", "test"]:
            for adapter in ["qwen7b_general", "qwen7b_short", "qwen7b_extreme_short"]:
                csv = cd_dir / f"{adapter}_{split}_predictions.csv"
                if csv.exists():
                    voters.setdefault(adapter, {})[split] = csv

    # 2. Zero-shot detectors
    zs_dir = ZERO_SHOT_ROOT / dataset
    if zs_dir.exists():
        for split in ["dev", "test"]:
            for name, fname in [
                ("fdgpt", f"{split}_fdgpt_scores.csv"),
                ("binoculars", f"{split}_bino_scores.csv"),
            ]:
                csv = zs_dir / fname
                if csv.exists():
                    voters.setdefault(name, {})[split] = csv

    # 3. PLM baselines (optional)
    plm_dir = PLM_ROOT / dataset
    if plm_dir.exists():
        for split in ["dev", "test"]:
            for name in ["bert", "roberta"]:
                csv = plm_dir / f"{name}_{split}_predictions.csv"
                if csv.exists():
                    voters.setdefault(name, {})[split] = csv

    return voters


def load_voter_df(voters, split):
    """Align all voter predictions for a given split into one DataFrame by row index.

    All inference scripts process the same jsonl in the same order, so index
    alignment is the most robust method (avoids Cartesian product on duplicate texts).
    """
    base_voter = list(voters.keys())[0]
    base_df = pd.read_csv(voters[base_voter][split])
    if "length" not in base_df.columns:
        base_df["length"] = base_df["text"].astype(str).apply(len)

    result = base_df[["text", "label", "length"]].copy()

    # Bring in id if any voter has it (needed for DS13 subset evaluation)
    for v in voters:
        tmp = pd.read_csv(voters[v][split])
        if "id" in tmp.columns:
            if len(tmp) != len(result):
                raise ValueError(f"Length mismatch for {v}: {len(tmp)} vs {len(result)}")
            result["id"] = tmp["id"].values
            break

    for voter_name, paths in voters.items():
        vdf = pd.read_csv(paths[split])
        if len(vdf) != len(result):
            raise ValueError(f"Length mismatch for {voter_name}: {len(vdf)} vs {len(result)}")
        col = VOTER_COLUMN_MAP.get(voter_name, "pred_prob")
        if col not in vdf.columns:
            raise ValueError(f"Expected column '{col}' not found in {paths[split]}")
        result[f"feat_{voter_name}"] = vdf[col].values

    return result.reset_index(drop=True)


def best_threshold_f1(y_true, probs):
    y_true = np.asarray(y_true)
    probs = np.asarray(probs)
    if len(y_true) == 0:
        return 0.5, 0.0
    # dynamic range based on score distribution
    p_min, p_max = probs.min(), probs.max()
    if p_min == p_max:
        return p_min, 0.0
    thresholds = np.linspace(p_min, p_max, 200)
    preds = (probs[:, None] >= thresholds).astype(int)
    tp = (preds * y_true[:, None]).sum(axis=0)
    fp = (preds * (1 - y_true[:, None])).sum(axis=0)
    fn = y_true.sum() - tp
    precision = tp / (tp + fp + 1e-9)
    recall = tp / (tp + fn + 1e-9)
    f1 = 2 * precision * recall / (precision + recall + 1e-9)
    best_idx = np.argmax(f1)
    return thresholds[best_idx], f1[best_idx]


def evaluate(y_true, probs, threshold=None):
    if threshold is None:
        threshold, _ = best_threshold_f1(y_true, probs)
    preds = (probs >= threshold).astype(int)
    return {
        "threshold": round(float(threshold), 4),
        "f1": round(f1_score(y_true, preds, zero_division=0), 6),
        "precision": round(precision_score(y_true, preds, zero_division=0), 6),
        "recall": round(recall_score(y_true, preds, zero_division=0), 6),
        "accuracy": round(accuracy_score(y_true, preds), 6),
    }


def bucket_name(length):
    if length <= 75:
        return "extreme_short"
    elif length <= 180:
        return "short"
    else:
        return "general"


# ---------------------------------------------------------------------------
# LR models
# ---------------------------------------------------------------------------
def fit_lr_global(df_dev, feature_cols, C=1.0):
    X = df_dev[feature_cols].copy()
    X["len_norm"] = df_dev["length"] / 1000.0
    y = df_dev["label"].values
    scaler = StandardScaler()
    Xs = scaler.fit_transform(X)
    clf = LogisticRegression(C=C, max_iter=1000, solver="lbfgs")
    clf.fit(Xs, y)
    probs = clf.predict_proba(Xs)[:, 1]
    th, _ = best_threshold_f1(y, probs)
    meta = {
        "feature_names": list(X.columns),
        "coef": clf.coef_[0].tolist(),
        "intercept": float(clf.intercept_[0]),
        "threshold": round(th, 4),
    }
    return scaler, clf, th, meta


def apply_lr_global(df, scaler, clf, feature_cols):
    X = df[feature_cols].copy()
    X["len_norm"] = df["length"] / 1000.0
    Xs = scaler.transform(X)
    return clf.predict_proba(Xs)[:, 1]


def fit_lr_bucket(df_dev, feature_cols, global_scaler, global_clf, global_th):
    bucket_models = {}
    bucket_thresholds = {}
    meta = {
        "global": {"threshold": global_th},
        "fallback": {},
    }
    df_dev = df_dev.copy()
    df_dev["bucket"] = df_dev["length"].apply(bucket_name)

    for bname, (lo, hi) in BUCKETS.items():
        sub = df_dev[(df_dev["length"] >= lo) & (df_dev["length"] <= hi)]
        if len(sub) >= MIN_SAMPLES_PER_BUCKET:
            X = sub[feature_cols].copy()
            X["len_norm"] = sub["length"] / 1000.0
            y = sub["label"].values
            scaler = StandardScaler()
            Xs = scaler.fit_transform(X)
            clf = LogisticRegression(C=1.0, max_iter=1000, solver="lbfgs")
            clf.fit(Xs, y)
            probs = clf.predict_proba(Xs)[:, 1]
            th, _ = best_threshold_f1(y, probs)
            bucket_models[bname] = (scaler, clf)
            bucket_thresholds[bname] = th
            meta[bname] = {"coef": clf.coef_[0].tolist(), "intercept": float(clf.intercept_[0]), "threshold": th, "n_dev": len(sub)}
            meta["fallback"][bname] = False
        else:
            bucket_models[bname] = (global_scaler, global_clf)
            bucket_thresholds[bname] = global_th
            meta[bname] = {"fallback_to_global": True, "n_dev": len(sub)}
            meta["fallback"][bname] = True

    def _apply(df):
        df = df.copy()
        df["bucket"] = df["length"].apply(bucket_name)
        probs = np.zeros(len(df))
        for bname, (lo, hi) in BUCKETS.items():
            mask = (df["length"] >= lo) & (df["length"] <= hi)
            if mask.any():
                scaler, clf = bucket_models[bname]
                X = df.loc[mask, feature_cols].copy()
                X["len_norm"] = df.loc[mask, "length"] / 1000.0
                Xs = scaler.transform(X)
                probs[mask] = clf.predict_proba(Xs)[:, 1]
        return probs

    return _apply, meta, bucket_thresholds


# ---------------------------------------------------------------------------
# Main
# ---------------------------------------------------------------------------
def main():
    import argparse
    parser = argparse.ArgumentParser()
    parser.add_argument("--dataset", required=True, help="e.g. DS06_External_core_balanced_v1 or DS13_NLPCC_full_test_v1")
    parser.add_argument("--fallback-dataset", default="DS06_External_core_balanced_v1",
                        help="If dev samples are insufficient, use this dataset's dev to train LR")
    args = parser.parse_args()

    voters = discover_voters(args.dataset)
    print(f"Discovered voters for {args.dataset}: {list(voters.keys())}")
    if not voters:
        print("No voter predictions found. Please run inference first. Exiting.")
        return

    df_dev = load_voter_df(voters, "dev")
    df_test = load_voter_df(voters, "test")
    print(f"Loaded dev={len(df_dev)} test={len(df_test)}")

    feature_cols = [c for c in df_dev.columns if c.startswith("feat_")]
    print(f"Feature columns ({len(feature_cols)}): {feature_cols}")

    # -----------------------------------------------------------------------
    # Global LR (with fallback if dev too small)
    # -----------------------------------------------------------------------
    print("\n[Global LR Stacking]")
    if len(df_dev) >= MIN_SAMPLES_GLOBAL_LR:
        scaler, clf, th, meta = fit_lr_global(df_dev, feature_cols)
        print(f"  Trained on {args.dataset} dev (n={len(df_dev)})")
    else:
        fb_voters = discover_voters(args.fallback_dataset)
        if not fb_voters:
            print(f"  Fallback dataset {args.fallback_dataset} has no voters; using current dev anyway")
            scaler, clf, th, meta = fit_lr_global(df_dev, feature_cols)
        else:
            fb_dev = load_voter_df(fb_voters, "dev")
            # intersect features
            fb_features = [c for c in fb_dev.columns if c.startswith("feat_")]
            common_features = list(set(feature_cols) & set(fb_features))
            if not common_features:
                print("  No common features between datasets; using current dev anyway")
                scaler, clf, th, meta = fit_lr_global(df_dev, feature_cols)
            else:
                print(f"  Dev too small (n={len(df_dev)}). Fallback LR trained on {args.fallback_dataset} dev (n={len(fb_dev)})")
                scaler, clf, th, meta = fit_lr_global(fb_dev, common_features)
                feature_cols = common_features
                # ensure any missing common features are present (should be rare)
                for c in common_features:
                    for d in (df_dev, df_test):
                        if c not in d.columns:
                            d[c] = np.nan

    print(f"  coef={meta['coef']}")
    print(f"  intercept={meta['intercept']}")
    print(f"  threshold={th}")

    summary = {}
    for split, df in [("dev", df_dev), ("test", df_test)]:
        probs = apply_lr_global(df, scaler, clf, feature_cols)
        metrics = evaluate(df["label"].values, probs, threshold=th)
        summary.setdefault("lr_global", {})[split] = metrics
        print(f"  {split}: f1={metrics['f1']:.4f} acc={metrics['accuracy']:.4f} prec={metrics['precision']:.4f} rec={metrics['recall']:.4f}")

    # -----------------------------------------------------------------------
    # LR Bucket
    # -----------------------------------------------------------------------
    print("\n[LR Bucket]")
    lb_fn, lb_meta, lb_ths = fit_lr_bucket(df_dev, feature_cols, scaler, clf, th)
    for split, df in [("dev", df_dev), ("test", df_test)]:
        probs = lb_fn(df)
        df_tmp = df.copy()
        df_tmp["prob"] = probs
        preds = np.zeros(len(df_tmp), dtype=int)
        for bname, (lo, hi) in BUCKETS.items():
            mask = (df_tmp["length"] >= lo) & (df_tmp["length"] <= hi)
            if mask.any():
                th_b = lb_ths[bname]
                preds[mask] = (df_tmp.loc[mask, "prob"].values >= th_b).astype(int)
        metrics = {
            "threshold": "per-bucket",
            "f1": round(f1_score(df_tmp["label"].values, preds, zero_division=0), 6),
            "precision": round(precision_score(df_tmp["label"].values, preds, zero_division=0), 6),
            "recall": round(recall_score(df_tmp["label"].values, preds, zero_division=0), 6),
            "accuracy": round(accuracy_score(df_tmp["label"].values, preds), 6),
        }
        summary.setdefault("lr_bucket", {})[split] = metrics
        print(f"  {split}: f1={metrics['f1']:.4f} acc={metrics['accuracy']:.4f} prec={metrics['precision']:.4f} rec={metrics['recall']:.4f}")

    # -----------------------------------------------------------------------
    # Per-subset evaluation for DS13
    # -----------------------------------------------------------------------
    if args.dataset == "DS13_NLPCC_full_test_v1" and "id" in df_test.columns:
        print("\n[DS13 Per-Subset LR Global]")
        probs_test = apply_lr_global(df_test, scaler, clf, feature_cols)
        df_test_eval = df_test.copy()
        df_test_eval["ensemble_prob"] = probs_test
        df_test_eval["ensemble_pred"] = (probs_test >= th).astype(int)
        subset_results = {}
        for sname, (lo, hi) in DS13_SUBSETS.items():
            sub = df_test_eval[(df_test_eval["id"] >= lo) & (df_test_eval["id"] <= hi)]
            if len(sub) == 0:
                continue
            y_true = sub["label"].values
            y_pred = sub["ensemble_pred"].values
            subset_results[sname] = {
                "n": len(sub),
                "f1": round(f1_score(y_true, y_pred, zero_division=0), 6),
                "precision": round(precision_score(y_true, y_pred, zero_division=0), 6),
                "recall": round(recall_score(y_true, y_pred, zero_division=0), 6),
                "accuracy": round(accuracy_score(y_true, y_pred), 6),
            }
            print(f"  {sname}: f1={subset_results[sname]['f1']:.4f} acc={subset_results[sname]['accuracy']:.4f}")
        summary["lr_global"]["subset"] = subset_results

    # -----------------------------------------------------------------------
    # Save outputs
    # -----------------------------------------------------------------------
    out_dir = OUT_ROOT / args.dataset
    out_dir.mkdir(parents=True, exist_ok=True)

    for split, df in [("dev", df_dev), ("test", df_test)]:
        probs = apply_lr_global(df, scaler, clf, feature_cols)
        df_out = df.copy()
        df_out["ensemble_prob"] = probs
        df_out["ensemble_pred"] = (probs >= th).astype(int)
        df_out.to_csv(out_dir / f"ensemble_lr_global_{split}_predictions.csv", index=False, encoding="utf-8")

        probs_b = lb_fn(df)
        df_out_b = df.copy()
        df_out_b["ensemble_prob"] = probs_b
        df_tmp = df_out_b.copy()
        df_tmp["prob"] = probs_b
        preds_b = np.zeros(len(df_tmp), dtype=int)
        for bname, (lo, hi) in BUCKETS.items():
            mask = (df_tmp["length"] >= lo) & (df_tmp["length"] <= hi)
            if mask.any():
                preds_b[mask] = (df_tmp.loc[mask, "prob"].values >= lb_ths[bname]).astype(int)
        df_out_b["ensemble_pred"] = preds_b
        df_out_b.to_csv(out_dir / f"ensemble_lr_bucket_{split}_predictions.csv", index=False, encoding="utf-8")

    with open(out_dir / "ensemble_meta.json", "w", encoding="utf-8") as f:
        json.dump({"lr_global": meta, "lr_bucket": lb_meta, "voters": list(voters.keys()), "summary": summary}, f, ensure_ascii=False, indent=2)

    print(f"\nSaved all outputs to {out_dir}")


if __name__ == "__main__":
    main()