scripts/metric.py

"""Compute Metrics between model scores and human-labeled scores.

Ground truth is loaded from the point-wise sampled file, where each item
exposes top-level fields ``if_score`` / ``vq_score`` / ``wc_score`` keyed by
``video_name``.
"""
import argparse
import json
from pathlib import Path

SCRIPT_DIR = Path(__file__).resolve().parent
PROJECT_ROOT = SCRIPT_DIR.parent
DEFAULT_RESULTS_DIR = PROJECT_ROOT / "results"
DEFAULT_GT_FILE = PROJECT_ROOT / "data" / "firm-video-bench.json"

MODEL_FILES = {
    "gemini-3.1-pro": "gemini31pro_scores.json",
    "gpt5": "gpt5_scores.json",
    "seed-2.0-lite": "seed20lite_scores.json",
    "qwen3vl-8b": "qwen3vl8b_scores.json",
    "qwen3vl-30b": "qwen3vl30ba3b_scores.json",
    "qwen3vl-235b": "qwen3vl235ba22b_scores.json",
    "internvl3-8b": "internvl3-8b_scores.json",
    "internvl3-38b": "internvl3-38b_scores.json",
    "firm-video-8b-qwen3vl": "firm-video-qwen3vl_scores.json",
    "firm-video-8b-internvl3": "firm-video-internvl3_scores.json"
}

# model dimension -> ground-truth key in GT_FILE
DIM_MAP = {
    "instruction_following": "if_score",
    "visual_quality": "vq_score",
    "world_consistency": "wc_score",
}


def load(path):
    with open(path, "r", encoding="utf-8") as f:
        return json.load(f)


def load_gt(path):
    """Return dict: video_name -> {if_score, vq_score, wc_score}."""
    gt = {}
    for item in load(path):
        key = item.get("video_name")
        if not key:
            continue
        gt[key] = {k: item.get(k) for k in DIM_MAP.values()}
    return gt


def _std(abs_errors):
    """绝对误差 |pred - human| 的样本标准差（围绕 MAE 的波动，ddof=1）。"""
    m = len(abs_errors)
    if m <= 1:
        return None
    mean_err = sum(abs_errors) / m
    return (sum((e - mean_err) ** 2 for e in abs_errors) / (m - 1)) ** 0.5


def _accuracy(abs_errors):
    """预测与 human GT 完全相等的比例。"""
    if not abs_errors:
        return None
    return sum(1 for e in abs_errors if e == 0) / len(abs_errors)


def _relaxed_accuracy(abs_errors):
    """预测与 human GT 相差不超过 1 的比例。"""
    if not abs_errors:
        return None
    return sum(1 for e in abs_errors if e <= 1) / len(abs_errors)


def _rankdata(values):
    """返回带 ties 平均秩（1-based）的秩数组。"""
    order = sorted(range(len(values)), key=lambda i: values[i])
    ranks = [0.0] * len(values)
    i = 0
    while i < len(values):
        j = i
        while j + 1 < len(values) and values[order[j + 1]] == values[order[i]]:
            j += 1
        avg_rank = (i + j) / 2.0 + 1.0
        for k in range(i, j + 1):
            ranks[order[k]] = avg_rank
        i = j + 1
    return ranks


def _spearman(pairs):
    """Spearman 秩相关系数（对秩做 Pearson，含 ties 处理）。pairs: [(pred, human)]。"""
    n = len(pairs)
    if n < 2:
        return None
    xs = [p for p, _ in pairs]
    ys = [h for _, h in pairs]
    rx = _rankdata(xs)
    ry = _rankdata(ys)
    mean_rx = sum(rx) / n
    mean_ry = sum(ry) / n
    cov = sum((a - mean_rx) * (b - mean_ry) for a, b in zip(rx, ry))
    var_x = sum((a - mean_rx) ** 2 for a in rx)
    var_y = sum((b - mean_ry) ** 2 for b in ry)
    denom = (var_x * var_y) ** 0.5
    if denom == 0:
        return None
    return cov / denom


def compute_mae(items, gt):
    """Return per-dimension (MAE, std, N), overall (MAE, std) and extra metrics.

    extra 部分返回 per-dimension 的 (accuracy, relaxed_accuracy, spearman)，
    overall 仅返回 (accuracy, relaxed_accuracy)。
    """
    diffs = {dim: [] for dim in DIM_MAP}
    pairs = {dim: [] for dim in DIM_MAP}
    missing = 0
    for item in items:
        key = item.get("video_name")
        human_scores = gt.get(key)
        if human_scores is None:
            missing += 1
            continue
        dims = {
            d["dimension"]: d.get("score")
            for d in item.get("scoring", {}).get("dimensions", [])
        }
        for model_dim, gt_key in DIM_MAP.items():
            human = human_scores.get(gt_key)
            pred = dims.get(model_dim)
            if human is None or pred is None:
                missing += 1
                continue
            try:
                pv = float(pred)
                hv = float(human)
            except (TypeError, ValueError):
                missing += 1
                continue
            diffs[model_dim].append(abs(pv - hv))
            pairs[model_dim].append((pv, hv))

    per_dim = {
        dim: (sum(v) / len(v) if v else None, _std(v), len(v))
        for dim, v in diffs.items()
    }
    all_diffs = [x for v in diffs.values() for x in v]
    overall = sum(all_diffs) / len(all_diffs) if all_diffs else None
    overall_std = _std(all_diffs)

    per_dim_extra = {
        dim: (_accuracy(diffs[dim]), _relaxed_accuracy(diffs[dim]), _spearman(pairs[dim]))
        for dim in DIM_MAP
    }
    overall_extra = (
        _accuracy(all_diffs),
        _relaxed_accuracy(all_diffs),
    )
    return per_dim, overall, overall_std, per_dim_extra, overall_extra, missing, len(items)


def parse_args():
    parser = argparse.ArgumentParser(
        description="Compute metrics between model scores and human-labeled scores."
    )
    parser.add_argument(
        "--gt_file",
        type=str,
        default=str(DEFAULT_GT_FILE),
        help="Ground-truth JSON file with if_score/vq_score/wc_score fields.",
    )
    parser.add_argument(
        "--results_dir",
        type=str,
        default=str(DEFAULT_RESULTS_DIR),
        help="Directory containing model score JSON files.",
    )
    return parser.parse_args()


def main():
    args = parse_args()
    gt_file = Path(args.gt_file).expanduser()
    results_dir = Path(args.results_dir).expanduser()

    if not gt_file.exists():
        print(f"GT file not found: {gt_file}")
        return
    gt = load_gt(gt_file)
    print(f"Loaded {len(gt)} GT entries from {gt_file}")

    fmt = lambda x: f"{x:.4f}" if x is not None else "  N/A "
    header = (
        f"{'Model':<12} {'N':>4} "
        f"{'IF':>10} {'IF_std':>10} "
        f"{'PQ':>10} {'PQ_std':>10} "
        f"{'WC':>10} {'WC_std':>10} "
        f"{'Overall':>10} {'Ovr_std':>10} {'missing':>8}"
    )
    print(header)
    print("-" * len(header))

    # 收集每个模型的额外指标
    extra_rows = []
    for name, fname in MODEL_FILES.items():
        path = results_dir / fname
        if not path.exists():
            print(f"{name}: file not found: {path}")
            continue
        items = load(path)
        (per_dim, overall, overall_std, per_dim_extra,
         overall_extra, missing, n) = compute_mae(items, gt)
        if_mae, if_std, _ = per_dim["instruction_following"]
        vq_mae, vq_std, _ = per_dim["visual_quality"]
        wc_mae, wc_std, _ = per_dim["world_consistency"]
        print(
            f"{name:<12} {n:>4} "
            f"{fmt(if_mae):>10} {fmt(if_std):>10} "
            f"{fmt(vq_mae):>10} {fmt(vq_std):>10} "
            f"{fmt(wc_mae):>10} {fmt(wc_std):>10} "
            f"{fmt(overall):>10} {fmt(overall_std):>10} {missing:>8}"
        )
        extra_rows.append((name, per_dim_extra, overall_extra))

    # ---- 额外指标：accuracy / relaxed accuracy / spearman ----
    if extra_rows:
        print("\n== Extra metrics: Accuracy(=) / Relaxed(|d|<=1) / Spearman ==")
        extra_header = (
            f"{'Model':<12} "
            f"{'IF_acc':>8} {'IF_racc':>8} {'IF_spr':>8} "
            f"{'PQ_acc':>8} {'PQ_racc':>8} {'PQ_spr':>8} "
            f"{'WC_acc':>8} {'WC_racc':>8} {'WC_spr':>8} "
            f"{'Ovr_acc':>8} {'Ovr_racc':>8}"
        )
        print(extra_header)
        print("-" * len(extra_header))
        for name, per_dim_extra, overall_extra in extra_rows:
            if_acc, if_racc, if_spr = per_dim_extra["instruction_following"]
            vq_acc, vq_racc, vq_spr = per_dim_extra["visual_quality"]
            wc_acc, wc_racc, wc_spr = per_dim_extra["world_consistency"]
            ovr_acc, ovr_racc = overall_extra
            print(
                f"{name:<12} "
                f"{fmt(if_acc):>8} {fmt(if_racc):>8} {fmt(if_spr):>8} "
                f"{fmt(vq_acc):>8} {fmt(vq_racc):>8} {fmt(vq_spr):>8} "
                f"{fmt(wc_acc):>8} {fmt(wc_racc):>8} {fmt(wc_spr):>8} "
                f"{fmt(ovr_acc):>8} {fmt(ovr_racc):>8}"
            )

    print("\nDimension mapping: instruction_following<->if_score, "
          "perceptual quality (PQ; input: visual_quality<->vq_score), "
          "world_coherence<->wc_score")
    print("std = 模型打分绝对误差|pred - human|的样本标准差(ddof=1)")
    print("acc = 完全相等准确率; racc = 相差<=1准确率; spr = Spearman秩相关系数")


if __name__ == "__main__":
    main()