Upload subgroup support-vs-profile analysis

evaluation/query_family/subgroup/support_vs_profile_analysis/README.md

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+# Support-vs-Profile Analysis
+
+这份目录专门回答一个问题：为什么 `subgroup_size_stability` 和 `internal_profile_stability` 的均值看起来很接近，但它们代表的能力其实很不一样。
+
+## 主图
+
+![Support vs profile density](D:\dpan\Uni\Project\HKUNAISS\SQLagent\Evaluation\query_fivepart_breakdown\subgroup_breakdown\support_vs_profile_analysis\support_vs_profile_panel_density.png)
+
+- 横坐标是 panel-level subgroup score。
+- 纵坐标是归一化后的相对密度；两条曲线各自的面积都被标准化为 `1`。
+- 这张图用的是 `dataset-model panel` 级别分数，而不是 query row 级别分数，因为 panel mean 更接近，更能突出“均值接近但分布形状不同”。
+
+## Query-level 版本
+
+![Support vs profile query density](D:\dpan\Uni\Project\HKUNAISS\SQLagent\Evaluation\query_fivepart_breakdown\subgroup_breakdown\support_vs_profile_analysis\support_vs_profile_query_density.png)
+
+- 这张图用的是 `query row` 级别分数。
+- `internal_profile_stability` 对应 `13406` 条 query。
+- `subgroup_size_stability` 对应 `9377` 条 query。
+- 两条曲线同样各自归一化到面积 `1`，所以纵轴表示的是相对密度，而不是绝对 query 数。
+- 如果按 query 去看，`size` 和 `internal` 的差异更明显：`size` 的中位数是 `0.55`，`internal` 的中位数是 `0.325`；`size` 有 `56.3%` 的 query 分数不低于 `0.5`，而 `internal` 只有 `40.8%`。
+
+## 图里最值得讲的点
+
+- panel-level 上，`internal_profile_stability` 的均值是 `0.398`，`subgroup_size_stability` 的均值是 `0.406`，两者确实很接近。
+- 但中位数差得更明显：`internal = 0.376`，`size = 0.468`。
+- `size` 分支更集中在中高分区域；`40.7%` 的 panel 分数不低于 `0.5`，而 `internal` 只有 `30.8%`。
+- `internal` 更容易掉到低分段；`25.9%` 的 panel 分数不高于 `0.2`，而 `size` 只有 `17.4%`。
+- 换句话说，`size` 不是均值高很多，而是“更稳、更少极低分、更容易保住基本盘”。
+
+## 这两条分支分别像什么能力
+
+`subgroup_size_stability` 更像一种 **support-preservation ability**：
+
+- subgroup 还在不在
+- subgroup 的大小/占比还大致对不对
+- top groups 的覆盖还大致在不在
+
+它对应的模板几乎都是 count-based：
+
+- `Filtered Top-k Group Count`，均值 `0.404`
+- `Grouped Count by Category`，均值 `0.420`
+- `Two-Dimensional Top-k Count`，均值 `0.422`
+
+这些查询要求模型保住的是“群体存在和粗质量分配”，而不是更细的内部分析结构。
+
+`internal_profile_stability` 更像一种 **within-group analytical structure ability**：
+
+- 组内排序还对不对
+- 组内 top-k summary 还稳不稳
+- 加了 filter 之后 subgroup profile 还在不在
+- subgroup 内部的局部二维结构和条件对比还在不在
+
+这类能力明显更脆弱。
+
+## 用三个模板举例说明为什么 internal 更难
+
+### 1. `Two-Dimensional Robust Summary`
+
+- 整体均值只有 `0.153`，中位数是 `0.000`
+- 它不是在问“某个 subgroup 还有多少人”，而是在问“这个 subgroup 在两个维度下的局部结构、稳健 summary 和过滤后轮廓还在不在”
+
+为什么难：
+
+- 只保住 subgroup mass 并不够
+- 只要组内局部 shape、局部排序、极值或二维对应关系漂了，这类题就会直接掉分
+- 所以它很容易出现“群体还在，但内部几何形状已经不对”
+
+### 2. `Grouped Summary Top-k`
+
+- 整体均值 `0.220`
+- 它关注的是 top-k groups 的 summary，而不是简单 count
+
+为什么难：
+
+- top-k 本身对组内顺序和相对大小很敏感
+- 一旦 synthetic data 把 group 内部的排序、相对差距或 summary 值抹平，top-k 结果就会变
+- 这意味着模型可能还保住了“哪些群体大概存在”，但保不住“谁才是真正排在前面的群体”
+
+### 3. `Two-Dimensional Summary with Filter`
+
+- 整体均值 `0.274`
+- 它会先做 filter，再看 subgroup 在二维下的局部 summary
+
+为什么难：
+
+- filter 会把分析问题从“全局粗结构”推进到“局部、条件化、支持更窄的子空间”
+- 在这个阶段，group 内部 profile 的细微偏移会被放大
+- 所以模型即使能把 subgroup size 保得还行，也可能保不住 filtered local view 下的 subgroup behavior
+
+## 这说明了什么
+
+最适合写进论文的结论是：
+
+- 当前模型相对更擅长保住 **subgroup support**，不那么擅长保住 **subgroup profile**
+- 也就是说，模型常常能保住“这个群体有多大”，但保不住“这个群体内部到底是什么样、谁更高、差多少、过滤以后还剩什么结构”
+
+这不是少数异常点。
+
+- `79/432` 个 dataset-model panels，也就是 `18.3%`，满足 `size - internal >= 0.10`
+- 在这批 panel 里，internal 最弱的 facet 是 `subgroup_rank_order`，均值只有 `0.255`
+- 最弱的 role 是 `filtered_stable_view`，均值 `0.263`
+
+## 对下游任务的含义
+
+这类 failure 在下游通常会表现为：
+
+- cohort sizing、segment prevalence、粗粒度 coverage summary 看起来还比较 plausible
+- 但 subgroup ranking、top-k subgroup selection、filtered subgroup comparison、group-specific KPI analysis、局部规则制定这类真正依赖组内 profile 的任务会更容易被误导
+
+一句话概括就是：
+
+**subgroup 的大小可以保住，但 subgroup 内部的分析形状往往保不住。**
+
+## 目录内容
+
+- `support_vs_profile_panel_density.png`
+- `support_vs_profile_panel_density.pdf`
+- `panel_score_density_curves.csv`
+- `summary_metrics.json`
+- `internal_templates_overall.csv`
+- `size_templates_overall.csv`
+- `dataset_model_branch_gaps.csv`
+- `strong_size_advantage_panels.csv`
+- `build_support_vs_profile_analysis.py`

evaluation/query_family/subgroup/support_vs_profile_analysis/build_support_vs_profile_analysis.py

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+from __future__ import annotations
+
+import json
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+
+try:
+    from scipy.stats import gaussian_kde
+except Exception:  # pragma: no cover - fallback if scipy is unavailable
+    gaussian_kde = None
+
+
+INTERNAL_COLOR = "#AA3377"
+SIZE_COLOR = "#009988"
+
+
+def _density_curve(values: np.ndarray, grid: np.ndarray) -> np.ndarray:
+    values = np.asarray(values, dtype=float)
+    values = values[np.isfinite(values)]
+    if values.size == 0:
+        return np.zeros_like(grid)
+
+    if gaussian_kde is not None and values.size >= 3 and np.std(values) > 0:
+        curve = gaussian_kde(values)(grid)
+    else:
+        bins = np.linspace(0.0, 1.0, 26)
+        density, edges = np.histogram(values, bins=bins, density=True)
+        centers = 0.5 * (edges[:-1] + edges[1:])
+        curve = np.interp(grid, centers, density, left=density[0], right=density[-1])
+
+    curve = np.clip(curve, 0.0, None)
+    area = np.trapz(curve, grid)
+    if area > 0:
+        curve = curve / area
+    return curve
+
+
+def _branch_summary(series: pd.Series) -> dict[str, float]:
+    return {
+        "count": int(series.size),
+        "mean": float(series.mean()),
+        "median": float(series.median()),
+        "p25": float(series.quantile(0.25)),
+        "p75": float(series.quantile(0.75)),
+        "share_le_0_1": float((series <= 0.1).mean()),
+        "share_le_0_2": float((series <= 0.2).mean()),
+        "share_ge_0_5": float((series >= 0.5).mean()),
+    }
+
+
+def main() -> None:
+    out_dir = Path(__file__).resolve().parent
+    project_root = Path(__file__).resolve().parents[4]
+    subgroup_root = project_root / "Evaluation" / "query_fivepart_breakdown" / "subgroup_breakdown"
+
+    dataset_model = pd.read_csv(subgroup_root / "data" / "dataset_model_scores.csv")
+    dataset_model = dataset_model[dataset_model["model_id"] != "real"].copy()
+
+    query_rows = pd.read_csv(subgroup_root / "data" / "subgroup_query_rows.csv")
+    query_rows = query_rows[query_rows["model_id"] != "real"].copy()
+
+    panel_internal = dataset_model["internal_profile_stability"].astype(float)
+    panel_size = dataset_model["subgroup_size_stability"].astype(float)
+    row_internal = query_rows.loc[
+        query_rows["subitem_id"] == "internal_profile_stability", "query_score"
+    ].astype(float)
+    row_size = query_rows.loc[
+        query_rows["subitem_id"] == "subgroup_size_stability", "query_score"
+    ].astype(float)
+
+    grid = np.linspace(0.0, 1.0, 500)
+    internal_density = _density_curve(panel_internal.to_numpy(), grid)
+    size_density = _density_curve(panel_size.to_numpy(), grid)
+
+    density_df = pd.DataFrame(
+        {
+            "score": grid,
+            "internal_profile_density": internal_density,
+            "subgroup_size_density": size_density,
+        }
+    )
+    density_df.to_csv(out_dir / "panel_score_density_curves.csv", index=False)
+
+    query_internal_density = _density_curve(row_internal.to_numpy(), grid)
+    query_size_density = _density_curve(row_size.to_numpy(), grid)
+    query_density_df = pd.DataFrame(
+        {
+            "score": grid,
+            "internal_profile_density": query_internal_density,
+            "subgroup_size_density": query_size_density,
+        }
+    )
+    query_density_df.to_csv(out_dir / "query_score_density_curves.csv", index=False)
+
+    internal_templates = (
+        query_rows[query_rows["subitem_id"] == "internal_profile_stability"]
+        .groupby(["template_id", "template_name"], as_index=False)
+        .agg(
+            rows=("query_score", "size"),
+            mean_score=("query_score", "mean"),
+            median_score=("query_score", "median"),
+            datasets=("dataset_id", "nunique"),
+        )
+        .sort_values(["mean_score", "rows"], ascending=[True, False])
+    )
+    internal_templates.to_csv(out_dir / "internal_templates_overall.csv", index=False)
+
+    size_templates = (
+        query_rows[query_rows["subitem_id"] == "subgroup_size_stability"]
+        .groupby(["template_id", "template_name"], as_index=False)
+        .agg(
+            rows=("query_score", "size"),
+            mean_score=("query_score", "mean"),
+            median_score=("query_score", "median"),
+            datasets=("dataset_id", "nunique"),
+        )
+        .sort_values(["mean_score", "rows"], ascending=[True, False])
+    )
+    size_templates.to_csv(out_dir / "size_templates_overall.csv", index=False)
+
+    strong_gap = dataset_model.copy()
+    strong_gap["size_minus_internal"] = (
+        strong_gap["subgroup_size_stability"] - strong_gap["internal_profile_stability"]
+    )
+    strong_gap.to_csv(out_dir / "dataset_model_branch_gaps.csv", index=False)
+
+    strong_size_adv = strong_gap[strong_gap["size_minus_internal"] >= 0.10].copy()
+    strong_size_adv.to_csv(out_dir / "strong_size_advantage_panels.csv", index=False)
+
+    internal_strong_gap = query_rows[
+        query_rows["subitem_id"] == "internal_profile_stability"
+    ].merge(
+        strong_size_adv[["dataset_id", "model_id"]],
+        on=["dataset_id", "model_id"],
+        how="inner",
+    )
+
+    strong_internal_templates = (
+        internal_strong_gap.groupby(["template_id", "template_name"], as_index=False)
+        .agg(rows=("query_score", "size"), mean_score=("query_score", "mean"))
+        .sort_values(["mean_score", "rows"], ascending=[True, False])
+    )
+    strong_internal_templates.to_csv(
+        out_dir / "internal_templates_in_strong_size_advantage_panels.csv", index=False
+    )
+
+    internal_facets = (
+        query_rows[query_rows["subitem_id"] == "internal_profile_stability"]
+        .groupby("intended_facet_id", as_index=False)
+        .agg(rows=("query_score", "size"), mean_score=("query_score", "mean"))
+        .sort_values("mean_score")
+    )
+    internal_facets.to_csv(out_dir / "internal_facets_overall.csv", index=False)
+
+    internal_roles = (
+        query_rows[query_rows["subitem_id"] == "internal_profile_stability"]
+        .groupby("variant_semantic_role", as_index=False)
+        .agg(rows=("query_score", "size"), mean_score=("query_score", "mean"))
+        .sort_values("mean_score")
+    )
+    internal_roles.to_csv(out_dir / "internal_roles_overall.csv", index=False)
+
+    strong_internal_facets = (
+        internal_strong_gap.groupby("intended_facet_id", as_index=False)
+        .agg(rows=("query_score", "size"), mean_score=("query_score", "mean"))
+        .sort_values("mean_score")
+    )
+    strong_internal_facets.to_csv(
+        out_dir / "internal_facets_in_strong_size_advantage_panels.csv", index=False
+    )
+
+    strong_internal_roles = (
+        internal_strong_gap.groupby("variant_semantic_role", as_index=False)
+        .agg(rows=("query_score", "size"), mean_score=("query_score", "mean"))
+        .sort_values("mean_score")
+    )
+    strong_internal_roles.to_csv(
+        out_dir / "internal_roles_in_strong_size_advantage_panels.csv", index=False
+    )
+
+    row_stats = {
+        "internal_profile": _branch_summary(row_internal),
+        "subgroup_size": _branch_summary(row_size),
+    }
+    panel_stats = {
+        "internal_profile": _branch_summary(panel_internal),
+        "subgroup_size": _branch_summary(panel_size),
+    }
+
+    metrics = {
+        "row_level": row_stats,
+        "panel_level": panel_stats,
+        "panel_count": int(len(dataset_model)),
+        "strong_size_advantage_panel_count": int(len(strong_size_adv)),
+        "strong_size_advantage_panel_share": float(len(strong_size_adv) / len(dataset_model)),
+        "strong_size_advantage_panel_share_ge_020": float(
+            (strong_gap["size_minus_internal"] >= 0.20).mean()
+        ),
+        "size_gt_internal_share": float(
+            (strong_gap["subgroup_size_stability"] > strong_gap["internal_profile_stability"]).mean()
+        ),
+        "internal_gt_size_share": float(
+            (strong_gap["internal_profile_stability"] > strong_gap["subgroup_size_stability"]).mean()
+        ),
+        "mean_size_minus_internal": float(strong_gap["size_minus_internal"].mean()),
+        "top_internal_templates": internal_templates.head(6).to_dict(orient="records"),
+        "top_size_templates": size_templates.to_dict(orient="records"),
+        "top_internal_facets": internal_facets.to_dict(orient="records"),
+        "top_internal_roles": internal_roles.to_dict(orient="records"),
+        "strong_gap_internal_templates": strong_internal_templates.head(6).to_dict(orient="records"),
+        "strong_gap_internal_facets": strong_internal_facets.to_dict(orient="records"),
+        "strong_gap_internal_roles": strong_internal_roles.to_dict(orient="records"),
+    }
+
+    with (out_dir / "summary_metrics.json").open("w", encoding="utf-8") as f:
+        json.dump(metrics, f, indent=2, ensure_ascii=False)
+
+    plt.style.use("seaborn-v0_8-whitegrid")
+    fig, ax = plt.subplots(figsize=(8.6, 5.3))
+    ax.fill_between(grid, internal_density, color=INTERNAL_COLOR, alpha=0.14)
+    ax.plot(grid, internal_density, color=INTERNAL_COLOR, linewidth=2.4, label="Internal profile stability")
+    ax.fill_between(grid, size_density, color=SIZE_COLOR, alpha=0.14)
+    ax.plot(grid, size_density, color=SIZE_COLOR, linewidth=2.4, label="Subgroup size stability")
+
+    mean_internal = panel_stats["internal_profile"]["mean"]
+    mean_size = panel_stats["subgroup_size"]["mean"]
+    ax.axvline(mean_internal, color=INTERNAL_COLOR, linestyle="--", linewidth=1.2, alpha=0.8)
+    ax.axvline(mean_size, color=SIZE_COLOR, linestyle="--", linewidth=1.2, alpha=0.8)
+
+    ax.set_xlim(0.0, 1.0)
+    ax.set_xlabel("Panel-level subgroup score")
+    ax.set_ylabel("Relative density (area = 1)")
+    ax.set_title("Support vs. profile: similar means, different score distributions")
+    ax.text(
+        0.015,
+        0.97,
+        (
+            f"Means: internal = {mean_internal:.3f}, size = {mean_size:.3f}\n"
+            f"Medians: internal = {panel_stats['internal_profile']['median']:.3f}, "
+            f"size = {panel_stats['subgroup_size']['median']:.3f}"
+        ),
+        transform=ax.transAxes,
+        va="top",
+        ha="left",
+        fontsize=9.5,
+        bbox={"boxstyle": "round,pad=0.25", "facecolor": "white", "edgecolor": "#CCCCCC", "alpha": 0.96},
+    )
+    ax.legend(frameon=False, loc="upper right")
+    ax.grid(True, color="#D8DDE3", linewidth=0.8, alpha=0.8)
+    ax.spines["top"].set_visible(False)
+    ax.spines["right"].set_visible(False)
+    fig.tight_layout()
+
+    fig.savefig(out_dir / "support_vs_profile_panel_density.png", dpi=240)
+    fig.savefig(out_dir / "support_vs_profile_panel_density.pdf")
+    plt.close(fig)
+
+    fig, ax = plt.subplots(figsize=(8.6, 5.3))
+    ax.fill_between(grid, query_internal_density, color=INTERNAL_COLOR, alpha=0.14)
+    ax.plot(
+        grid,
+        query_internal_density,
+        color=INTERNAL_COLOR,
+        linewidth=2.4,
+        label="Internal profile stability",
+    )
+    ax.fill_between(grid, query_size_density, color=SIZE_COLOR, alpha=0.14)
+    ax.plot(
+        grid,
+        query_size_density,
+        color=SIZE_COLOR,
+        linewidth=2.4,
+        label="Subgroup size stability",
+    )
+
+    row_mean_internal = row_stats["internal_profile"]["mean"]
+    row_mean_size = row_stats["subgroup_size"]["mean"]
+    ax.axvline(row_mean_internal, color=INTERNAL_COLOR, linestyle="--", linewidth=1.2, alpha=0.8)
+    ax.axvline(row_mean_size, color=SIZE_COLOR, linestyle="--", linewidth=1.2, alpha=0.8)
+
+    ax.set_xlim(0.0, 1.0)
+    ax.set_xlabel("Query-level subgroup score")
+    ax.set_ylabel("Relative density (area = 1)")
+    ax.set_title("Support vs. profile at the query level")
+    ax.text(
+        0.015,
+        0.97,
+        (
+            f"Queries: internal = {row_stats['internal_profile']['count']}, "
+            f"size = {row_stats['subgroup_size']['count']}\n"
+            f"Means: internal = {row_mean_internal:.3f}, size = {row_mean_size:.3f}\n"
+            f"Medians: internal = {row_stats['internal_profile']['median']:.3f}, "
+            f"size = {row_stats['subgroup_size']['median']:.3f}"
+        ),
+        transform=ax.transAxes,
+        va="top",
+        ha="left",
+        fontsize=9.0,
+        bbox={"boxstyle": "round,pad=0.25", "facecolor": "white", "edgecolor": "#CCCCCC", "alpha": 0.96},
+    )
+    ax.legend(frameon=False, loc="upper right")
+    ax.grid(True, color="#D8DDE3", linewidth=0.8, alpha=0.8)
+    ax.spines["top"].set_visible(False)
+    ax.spines["right"].set_visible(False)
+    fig.tight_layout()
+
+    fig.savefig(out_dir / "support_vs_profile_query_density.png", dpi=240)
+    fig.savefig(out_dir / "support_vs_profile_query_density.pdf")
+    plt.close(fig)
+
+
+if __name__ == "__main__":
+    main()

evaluation/query_family/subgroup/support_vs_profile_analysis/dataset_model_branch_gaps.csv

@@ -0,0 +1,433 @@
+dataset_id,dataset_prefix,model_id,model_label,internal_profile_stability,subgroup_size_stability,internal_profile_stability__query_count,subgroup_size_stability__query_count,subgroup_structure_score,profile_minus_size,active_subitem_count,size_minus_internal
+c2,c,arf,ARF,1.0,0.550712393939394,25.0,33.0,0.7753561969696969,0.449287606060606,2,-0.449287606060606
+c2,c,bayesnet,BayesNet,1.0,0.5463347575757577,25.0,33.0,0.7731673787878788,0.4536652424242423,2,-0.45366524242424233
+c2,c,ctgan,CTGAN,1.0,0.5425895757575758,25.0,33.0,0.7712947878787879,0.4574104242424242,2,-0.4574104242424242
+c2,c,forestdiffusion,ForestDiffusion,0.16766668,0.1418831212121212,25.0,33.0,0.1547749006060606,0.0257835587878788,2,-0.025783558787878802
+c2,c,realtabformer,RealTabFormer,1.0,0.5611173636363637,25.0,33.0,0.7805586818181818,0.4388826363636363,2,-0.43888263636363634
+c2,c,tabbyflow,TabbyFlow,0.23813336,0.153810606060606,25.0,33.0,0.195971983030303,0.0843227539393939,2,-0.08432275393939398
+c2,c,tabddpm,TabDDPM,0.177,0.1449035757575757,25.0,33.0,0.1609517878787879,0.0320964242424242,2,-0.03209642424242429
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evaluation/query_family/subgroup/support_vs_profile_analysis/internal_facets_in_strong_size_advantage_panels.csv

@@ -0,0 +1,4 @@
+intended_facet_id,rows,mean_score
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+subgroup_distribution_shift,721,0.28187558529819695
+subgroup_conditional_contrast,606,0.2919050693069307

evaluation/query_family/subgroup/support_vs_profile_analysis/internal_facets_overall.csv

@@ -0,0 +1,4 @@
+intended_facet_id,rows,mean_score
+subgroup_rank_order,4742,0.3844953728384648
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+subgroup_distribution_shift,4639,0.394163997844363

evaluation/query_family/subgroup/support_vs_profile_analysis/internal_roles_in_strong_size_advantage_panels.csv

@@ -0,0 +1,4 @@
+variant_semantic_role,rows,mean_score
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+collapsed_target_view,675,0.2898070740740741

evaluation/query_family/subgroup/support_vs_profile_analysis/internal_roles_overall.csv

@@ -0,0 +1,4 @@
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evaluation/query_family/subgroup/support_vs_profile_analysis/internal_templates_in_strong_size_advantage_panels.csv

@@ -0,0 +1,11 @@
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evaluation/query_family/subgroup/support_vs_profile_analysis/internal_templates_overall.csv

@@ -0,0 +1,11 @@
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+tpl_clickbench_group_distinct_topk,Top-k Groups by Distinct Entity Coverage,982,0.6205345692464359,0.516667,10

evaluation/query_family/subgroup/support_vs_profile_analysis/panel_score_density_curves.csv

@@ -0,0 +1,501 @@
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evaluation/query_family/subgroup/support_vs_profile_analysis/score_cluster_analysis.md

@@ -0,0 +1,105 @@
+## Why do subgroup query scores pile up around 0.5-0.6?
+
+The concentration is real. It is not a KDE artifact.
+
+At the query level, the most common exact score is `0.55`:
+
+- `internal_profile_stability`: `3789 / 13406` queries (`28.3%`)
+- `subgroup_size_stability`: `4550 / 9377` queries (`48.5%`)
+
+More broadly, the whole `(0.5, 0.6]` band contains:
+
+- `31.97%` of `internal_profile_stability` queries
+- `54.63%` of `subgroup_size_stability` queries
+
+### The direct reason
+
+The result-comparison function is defined in [src/evaluation/real_panel_experiment.py](D:\dpan\Uni\Project\HKUNAISS\SQLagent\src\evaluation\real_panel_experiment.py:444):
+
+```python
+score = (
+    0.45 * strict_set_score
+    + 0.20 * key_set_score
+    + 0.15 * profile_score
+    + 0.10 * row_count_score
+    + 0.10 * column_score
+)
+```
+
+The crucial detail is that `profile_score` is computed only on the inferred key columns, not on the measure columns such as `count`, `sum`, or `mean`; see [_column_profile_score](D:\dpan\Uni\Project\HKUNAISS\SQLagent\src\evaluation\real_panel_experiment.py:412).
+
+This creates a very common "structure right, values wrong" scoring state:
+
+- `strict_set_score = 0`
+- `key_set_score = 1`
+- `profile_score = 1`
+- `row_count_score = 1`
+- `column_score = 1`
+
+That state maps exactly to:
+
+`0.45 * 0 + 0.20 * 1 + 0.15 * 1 + 0.10 * 1 + 0.10 * 1 = 0.55`
+
+### What this means semantically
+
+This pattern says:
+
+- the query returned the right columns
+- the number of rows is right
+- the same subgroup keys are present
+- the marginal distribution over key columns looks right
+- but the full output rows do not exactly match
+
+For subgroup queries, that usually means the model recovered the subgroup scaffold, but not the exact subgroup-level values.
+
+Examples:
+
+- `Grouped Count by Category`: the same categories appear, but their exact counts differ
+- `Support-Guarded Group Average`: the same groups survive the support threshold, but the group averages differ
+- `Top-k Group Sum with Filter`: the same filtered groups appear, but the summed values are not exact
+
+Once a measure column differs, the whole row tuple differs, so `strict_set_score` can collapse to `0` even when the group identities are preserved.
+
+### Why the whole band is dense, not only the exact point 0.55
+
+Near-`0.55` scores are small perturbations of the same pattern.
+
+Common examples observed in the raw query logs:
+
+- `strict=0`, `key=profile=row_count=0.9`, `column=1` gives `0.505`
+- `strict=0`, `key=profile=row_count=0.9167`, `column=1` gives about `0.5125`
+- `strict=0.0909`, others `=1`, gives about `0.5909`
+
+So the full `(0.5, 0.6]` band mostly represents "almost perfect subgroup structure recovery, but weak exact value recovery."
+
+### Why this is especially common for subgroup queries
+
+Subgroup workloads are dominated by grouped summaries:
+
+- size-side templates:
+  - `Grouped Count by Category`
+  - `Filtered Top-k Group Count`
+  - `Two-Dimensional Top-k Count`
+- internal-side templates:
+  - `Support-Guarded Group Average`
+  - `Grouped Numeric Mean`
+  - `Grouped Numeric Sum`
+  - `Top-k Group Sum with Filter`
+
+For these templates, it is much easier for a synthetic table to preserve:
+
+- which groups exist
+- how many groups survive
+- which columns appear
+
+than to preserve the exact aggregated values for every group.
+
+So the scoring function repeatedly assigns partial credit for getting the subgroup structure right while missing the subgroup values, and this repeatedly lands in the `0.5-0.6` region.
+
+### Short takeaway
+
+The `0.5-0.6` concentration is evidence of a very specific failure mode:
+
+models often preserve subgroup support and subgroup identity, but not exact subgroup-level analytical values.
+
+That is exactly why the distribution can look heavily concentrated in the mid-high band even when true within-group analytical fidelity is still limited.

evaluation/query_family/subgroup/support_vs_profile_analysis/size_templates_overall.csv

@@ -0,0 +1,4 @@
+template_id,template_name,rows,mean_score,median_score,datasets
+tpl_clickbench_filtered_topk_group_count,Filtered Top-k Group Count,5654,0.40358913937035734,0.55,36
+tpl_clickbench_group_count,Grouped Count by Category,3116,0.41976913478819,0.55,46
+tpl_clickbench_two_dimensional_topk_count,Two-Dimensional Top-k Count,607,0.42170430477759474,0.486364,9

evaluation/query_family/subgroup/support_vs_profile_analysis/strong_size_advantage_panels.csv

@@ -0,0 +1,80 @@
+dataset_id,dataset_prefix,model_id,model_label,internal_profile_stability,subgroup_size_stability,internal_profile_stability__query_count,subgroup_size_stability__query_count,subgroup_structure_score,profile_minus_size,active_subitem_count,size_minus_internal
+c3,c,arf,ARF,0.275,0.55,21.0,23.0,0.4125,-0.275,2,0.275
+c3,c,bayesnet,BayesNet,0.275,0.6752173913043479,21.0,23.0,0.4751086956521739,-0.4002173913043478,2,0.40021739130434786
+c3,c,ctgan,CTGAN,0.275,0.55,21.0,23.0,0.4125,-0.275,2,0.275
+c3,c,realtabformer,RealTabFormer,0.275,0.55,21.0,23.0,0.4125,-0.275,2,0.275
+c3,c,tabbyflow,TabbyFlow,0.275,0.55,21.0,23.0,0.4125,-0.275,2,0.275
+c3,c,tabddpm,TabDDPM,0.275,0.55,21.0,23.0,0.4125,-0.275,2,0.275
+c3,c,tabdiff,TabDiff,0.275,0.55,21.0,23.0,0.4125,-0.275,2,0.275
+c3,c,tabpfgen,TabPFGen,0.275,0.55,21.0,23.0,0.4125,-0.275,2,0.275
+c3,c,tvae,TVAE,0.275,0.55,21.0,23.0,0.4125,-0.275,2,0.275
+c6,c,arf,ARF,0.3651625714285714,0.5124462068965517,21.0,29.0,0.4388043891625616,-0.1472836354679803,2,0.14728363546798035
+c6,c,bayesnet,BayesNet,0.4284299523809524,0.5314501724137931,21.0,29.0,0.4799400623973727,-0.1030202200328407,2,0.10302022003284073
+c6,c,tabbyflow,TabbyFlow,0.3417297142857143,0.5043883103448276,21.0,29.0,0.4230590123152709,-0.1626585960591132,2,0.16265859605911326
+c6,c,tabdiff,TabDiff,0.362113619047619,0.4963698620689655,21.0,29.0,0.4292417405582923,-0.1342562430213465,2,0.13425624302134648
+c6,c,tabpfgen,TabPFGen,0.3854356190476191,0.5351597586206897,21.0,29.0,0.4602976888341543,-0.1497241395730705,2,0.14972413957307057
+c6,c,tabsyn,TabSyn,0.3893078571428572,0.5252205172413793,21.0,29.0,0.4572641871921182,-0.1359126600985221,2,0.1359126600985221
+c9,c,arf,ARF,0.23787228,0.4193787575757576,25.0,33.0,0.3286255187878788,-0.1815064775757575,2,0.1815064775757576
+c9,c,realtabformer,RealTabFormer,0.36372132,0.5239933333333333,25.0,33.0,0.4438573266666666,-0.1602720133333333,2,0.1602720133333333
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evaluation/query_family/subgroup/support_vs_profile_analysis/summary_metrics.json

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evaluation/query_family/subgroup/support_vs_profile_analysis/support_vs_profile_panel_density.pdf

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