Upload subgroup support3 analysis

.gitattributes

@@ -23,3 +23,4 @@ evaluation/query_family/missingness/regime_diagnostic/manifest.json filter=lfs d
 evaluation/tail/breakdown/v2/model_summary__v2.csv filter=lfs diff=lfs merge=lfs -text
 evaluation/tail/breakdown/v2/prefix_summary__v2.csv filter=lfs diff=lfs merge=lfs -text
 evaluation/query_family/subgroup/data/subgroup_query_rows.csv filter=lfs diff=lfs merge=lfs -text
+evaluation/query_family/subgroup/support3_analysis/query_support3_rows.csv filter=lfs diff=lfs merge=lfs -text

evaluation/query_family/subgroup/support3_analysis/README.md

@@ -0,0 +1,86 @@
+## Support3 analysis
+
+This directory reframes the subgroup comparison without using the original weighted composite query score from [real_panel_experiment.py](D:\dpan\Uni\Project\HKUNAISS\SQLagent\src\evaluation\real_panel_experiment.py:471).
+
+### Why `support3_score`
+
+We define:
+
+`support3_score = mean(key_set_score, row_count_score, column_score)`
+
+This is the cleanest lightweight proxy for subgroup support preservation because it directly asks:
+
+- did the same subgroup keys appear?
+- did a similar number of grouped rows survive?
+- did the output keep the same column scaffold?
+
+It avoids:
+
+- the original hand-set weights
+- `strict_set_score`, which is dominated by exact value matching
+- the ambiguous use of `profile_score` in the implementation, which is really a key-column marginal score rather than a subgroup analytical-value score
+
+### What survives after removing the original weights
+
+The core claim still survives:
+
+`subgroup_size_stability` is better preserved than `internal_profile_stability` in a support-preservation sense.
+
+At the query level:
+
+- internal mean: `0.755`
+- size mean: `0.818`
+- mean gap: `+0.063` in favor of size
+
+At the panel level:
+
+- internal mean: `0.754`
+- size mean: `0.810`
+- mean gap: `+0.055` in favor of size
+
+### Can we also say size changes less?
+
+Not in every possible sense.
+
+Across the three regimes (`c`, `m`, `n`), the absolute mean range is not smaller for size:
+
+- internal panel means: `0.759 -> 0.818 -> 0.709`, range `0.110`
+- size panel means: `0.811 -> 0.881 -> 0.752`, range `0.129`
+
+So the strict statement:
+
+"size has smaller regime-to-regime movement"
+
+is **not** the safest one.
+
+The safer and better-supported statement is:
+
+`size` has a **lighter low-score tail** and **lower relative dispersion** under `support3_score`.
+
+Evidence:
+
+- panel-level CV:
+  - internal: `0.265`
+  - size: `0.246`
+- panel-level median absolute deviation from the median:
+  - internal: `0.180`
+  - size: `0.102`
+- panel share with support3 `<= 0.4`:
+  - internal: `5.8%`
+  - size: `2.7%`
+- panel share with support3 `<= 0.8`:
+  - internal: `54.2%`
+  - size: `38.3%`
+
+So the stronger reading is:
+
+size is not necessarily flatter across all regimes, but it is more resistant to severe degradation.
+
+### Files
+
+- [build_support3_analysis.py](D:\dpan\Uni\Project\HKUNAISS\SQLagent\Evaluation\query_fivepart_breakdown\subgroup_breakdown\support3_analysis\build_support3_analysis.py)
+- [support3_query_density.png](D:\dpan\Uni\Project\HKUNAISS\SQLagent\Evaluation\query_fivepart_breakdown\subgroup_breakdown\support3_analysis\support3_query_density.png)
+- [support3_panel_density.png](D:\dpan\Uni\Project\HKUNAISS\SQLagent\Evaluation\query_fivepart_breakdown\subgroup_breakdown\support3_analysis\support3_panel_density.png)
+- [support3_panel_ecdf.png](D:\dpan\Uni\Project\HKUNAISS\SQLagent\Evaluation\query_fivepart_breakdown\subgroup_breakdown\support3_analysis\support3_panel_ecdf.png)
+- [support3_panel_thresholds.png](D:\dpan\Uni\Project\HKUNAISS\SQLagent\Evaluation\query_fivepart_breakdown\subgroup_breakdown\support3_analysis\support3_panel_thresholds.png)
+- [summary_metrics.json](D:\dpan\Uni\Project\HKUNAISS\SQLagent\Evaluation\query_fivepart_breakdown\subgroup_breakdown\support3_analysis\summary_metrics.json)

evaluation/query_family/subgroup/support3_analysis/build_support3_analysis.py

@@ -0,0 +1,264 @@
+from __future__ import annotations
+
+import json
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+
+
+PROJECT_ROOT = Path(r"D:\dpan\Uni\Project\HKUNAISS\SQLagent")
+SUBGROUP_DATA_PATH = (
+    PROJECT_ROOT
+    / "Evaluation"
+    / "query_fivepart_breakdown"
+    / "subgroup_breakdown"
+    / "data"
+    / "subgroup_query_rows.csv"
+)
+ANALYSIS_RUN_DIR = (
+    PROJECT_ROOT
+    / "Evaluation"
+    / "analysis"
+    / "runs"
+    / "eval_refresh_20260503_1730_zurich"
+    / "datasets"
+)
+OUT_DIR = (
+    PROJECT_ROOT
+    / "Evaluation"
+    / "query_fivepart_breakdown"
+    / "subgroup_breakdown"
+    / "support3_analysis"
+)
+
+SIZE_ID = "subgroup_size_stability"
+INTERNAL_ID = "internal_profile_stability"
+PALETTE = {
+    INTERNAL_ID: "#8b2f5d",
+    SIZE_ID: "#1f7a5c",
+}
+
+
+def load_rows() -> pd.DataFrame:
+    df = pd.read_csv(SUBGROUP_DATA_PATH)
+    df = df[df["model_id"] != "real"].copy()
+    return df
+
+
+def load_details(sub_df: pd.DataFrame) -> pd.DataFrame:
+    needed = set(zip(sub_df["dataset_id"], sub_df["model_id"], sub_df["asset_key"], sub_df["query_id"]))
+    rows: list[dict[str, object]] = []
+    for dataset_id in sorted(sub_df["dataset_id"].dropna().unique()):
+        path = ANALYSIS_RUN_DIR / str(dataset_id) / f"analysis_query_scores__{dataset_id}.jsonl"
+        with path.open("r", encoding="utf-8") as handle:
+            for line in handle:
+                obj = json.loads(line)
+                key = (obj["dataset_id"], obj["model_id"], obj["asset_key"], obj["query_id"])
+                if key not in needed:
+                    continue
+                details = obj.get("details") or {}
+                rows.append(
+                    {
+                        "dataset_id": obj["dataset_id"],
+                        "model_id": obj["model_id"],
+                        "asset_key": obj["asset_key"],
+                        "query_id": obj["query_id"],
+                        "key_set_score": details.get("key_set_score"),
+                        "row_count_score": details.get("row_count_score"),
+                        "column_score": details.get("column_score"),
+                    }
+                )
+    return pd.DataFrame(rows)
+
+
+def add_support3(df: pd.DataFrame) -> pd.DataFrame:
+    out = df.copy()
+    for col in ["key_set_score", "row_count_score", "column_score"]:
+        out[col] = pd.to_numeric(out[col], errors="coerce")
+    out["support3_score"] = out[["key_set_score", "row_count_score", "column_score"]].mean(axis=1)
+    return out
+
+
+def kde_curve(values: np.ndarray, grid: np.ndarray, bandwidth: float = 0.045) -> np.ndarray:
+    values = values[np.isfinite(values)]
+    if len(values) == 0:
+        return np.zeros_like(grid)
+    diffs = grid[:, None] - values[None, :]
+    kernel = np.exp(-0.5 * (diffs / bandwidth) ** 2) / (bandwidth * np.sqrt(2 * np.pi))
+    density = kernel.mean(axis=1)
+    area = np.trapz(density, grid)
+    if area > 0:
+        density = density / area
+    return density
+
+
+def save_density_plot(
+    values_by_subitem: dict[str, np.ndarray],
+    out_path: Path,
+    title: str,
+    x_label: str,
+    summary_text: str,
+) -> pd.DataFrame:
+    grid = np.linspace(0, 1, 500)
+    rows: list[dict[str, float | str]] = []
+    plt.figure(figsize=(8.6, 5.2))
+    for subitem_id, values in values_by_subitem.items():
+        density = kde_curve(values, grid)
+        label = "Internal profile" if subitem_id == INTERNAL_ID else "Subgroup size"
+        plt.plot(grid, density, linewidth=2.4, color=PALETTE[subitem_id], label=label)
+        plt.fill_between(grid, density, alpha=0.16, color=PALETTE[subitem_id])
+        for x, y in zip(grid, density):
+            rows.append({"subitem_id": subitem_id, "score": float(x), "density": float(y)})
+
+    plt.xlim(0, 1)
+    plt.xlabel(x_label)
+    plt.ylabel("Relative density (area = 1)")
+    plt.title(title)
+    plt.legend(frameon=False)
+    plt.text(
+        0.985,
+        0.97,
+        summary_text,
+        transform=plt.gca().transAxes,
+        ha="right",
+        va="top",
+        fontsize=9.5,
+        bbox={"boxstyle": "round,pad=0.35", "facecolor": "white", "alpha": 0.9, "edgecolor": "#cccccc"},
+    )
+    plt.tight_layout()
+    plt.savefig(out_path, dpi=220, bbox_inches="tight")
+    plt.close()
+    return pd.DataFrame(rows)
+
+
+def save_ecdf_plot(panel_df: pd.DataFrame, out_path: Path) -> pd.DataFrame:
+    rows: list[dict[str, float | str]] = []
+    plt.figure(figsize=(8.4, 5.0))
+    for subitem_id in [INTERNAL_ID, SIZE_ID]:
+        values = np.sort(panel_df.loc[panel_df["subitem_id"] == subitem_id, "support3_score"].dropna().to_numpy())
+        probs = np.arange(1, len(values) + 1) / len(values)
+        label = "Internal profile" if subitem_id == INTERNAL_ID else "Subgroup size"
+        plt.step(values, probs, where="post", linewidth=2.4, color=PALETTE[subitem_id], label=label)
+        for x, y in zip(values, probs):
+            rows.append({"subitem_id": subitem_id, "score": float(x), "cdf": float(y)})
+    plt.xlim(0, 1)
+    plt.ylim(0, 1)
+    plt.xlabel("Panel-level support3 score")
+    plt.ylabel("Cumulative share of panels")
+    plt.title("Support3 ECDF: size has a lighter low-score tail")
+    plt.legend(frameon=False, loc="lower right")
+    plt.tight_layout()
+    plt.savefig(out_path, dpi=220, bbox_inches="tight")
+    plt.close()
+    return pd.DataFrame(rows)
+
+
+def save_threshold_bar_plot(panel_df: pd.DataFrame, out_path: Path) -> pd.DataFrame:
+    thresholds = [0.4, 0.6, 0.8]
+    out_rows: list[dict[str, float | str]] = []
+    fig, ax = plt.subplots(figsize=(8.2, 4.8))
+    x = np.arange(len(thresholds))
+    width = 0.34
+    for offset, subitem_id in [(-width / 2, INTERNAL_ID), (width / 2, SIZE_ID)]:
+        shares = []
+        for thr in thresholds:
+            values = panel_df.loc[panel_df["subitem_id"] == subitem_id, "support3_score"]
+            share = float((values <= thr).mean())
+            shares.append(share)
+            out_rows.append({"subitem_id": subitem_id, "threshold": thr, "share_leq": share})
+        label = "Internal profile" if subitem_id == INTERNAL_ID else "Subgroup size"
+        ax.bar(x + offset, shares, width=width, color=PALETTE[subitem_id], alpha=0.9, label=label)
+    ax.set_xticks(x, [f"<= {thr:.1f}" for thr in thresholds])
+    ax.set_ylim(0, 0.65)
+    ax.set_ylabel("Share of panels")
+    ax.set_title("Low-score tail under support3")
+    ax.legend(frameon=False)
+    plt.tight_layout()
+    plt.savefig(out_path, dpi=220, bbox_inches="tight")
+    plt.close()
+    return pd.DataFrame(out_rows)
+
+
+def build_summary(query_df: pd.DataFrame, panel_df: pd.DataFrame) -> dict[str, object]:
+    summary: dict[str, object] = {}
+    for level_name, frame in [("query", query_df), ("panel", panel_df)]:
+        for subitem_id in [INTERNAL_ID, SIZE_ID]:
+            part = frame[frame["subitem_id"] == subitem_id]["support3_score"].dropna()
+            prefix = f"{level_name}__{subitem_id}"
+            summary[f"{prefix}__mean"] = round(float(part.mean()), 6)
+            summary[f"{prefix}__median"] = round(float(part.median()), 6)
+            summary[f"{prefix}__std"] = round(float(part.std()), 6)
+            summary[f"{prefix}__q1"] = round(float(part.quantile(0.25)), 6)
+            summary[f"{prefix}__q3"] = round(float(part.quantile(0.75)), 6)
+            summary[f"{prefix}__cv"] = round(float(part.std() / part.mean()), 6)
+        internal = frame[frame["subitem_id"] == INTERNAL_ID]["support3_score"].dropna()
+        size = frame[frame["subitem_id"] == SIZE_ID]["support3_score"].dropna()
+        summary[f"{level_name}__mean_gap_size_minus_internal"] = round(float(size.mean() - internal.mean()), 6)
+    prefix_means = (
+        panel_df.groupby(["dataset_prefix", "subitem_id"])["support3_score"].mean().reset_index().round(6)
+    )
+    summary["panel_prefix_means"] = prefix_means.to_dict(orient="records")
+    return summary
+
+
+def main() -> None:
+    OUT_DIR.mkdir(parents=True, exist_ok=True)
+    subgroup = load_rows()
+    details = load_details(subgroup)
+    merged = subgroup.merge(details, on=["dataset_id", "model_id", "asset_key", "query_id"], how="left")
+    merged = add_support3(merged)
+
+    panel_df = (
+        merged.groupby(["dataset_prefix", "dataset_id", "model_id", "subitem_id"], as_index=False)["support3_score"]
+        .mean()
+        .round(6)
+    )
+
+    query_summary_text = (
+        f"Query-level means\n"
+        f"Internal: {merged.loc[merged['subitem_id']==INTERNAL_ID, 'support3_score'].mean():.3f}\n"
+        f"Size: {merged.loc[merged['subitem_id']==SIZE_ID, 'support3_score'].mean():.3f}"
+    )
+    panel_summary_text = (
+        f"Panel-level means\n"
+        f"Internal: {panel_df.loc[panel_df['subitem_id']==INTERNAL_ID, 'support3_score'].mean():.3f}\n"
+        f"Size: {panel_df.loc[panel_df['subitem_id']==SIZE_ID, 'support3_score'].mean():.3f}"
+    )
+
+    query_curves = save_density_plot(
+        {
+            INTERNAL_ID: merged.loc[merged["subitem_id"] == INTERNAL_ID, "support3_score"].dropna().to_numpy(),
+            SIZE_ID: merged.loc[merged["subitem_id"] == SIZE_ID, "support3_score"].dropna().to_numpy(),
+        },
+        OUT_DIR / "support3_query_density.png",
+        "Support-preservation score at the query level",
+        "Query-level support3 score",
+        query_summary_text,
+    )
+    panel_curves = save_density_plot(
+        {
+            INTERNAL_ID: panel_df.loc[panel_df["subitem_id"] == INTERNAL_ID, "support3_score"].dropna().to_numpy(),
+            SIZE_ID: panel_df.loc[panel_df["subitem_id"] == SIZE_ID, "support3_score"].dropna().to_numpy(),
+        },
+        OUT_DIR / "support3_panel_density.png",
+        "Support-preservation score at the panel level",
+        "Panel-level support3 score",
+        panel_summary_text,
+    )
+    ecdf = save_ecdf_plot(panel_df, OUT_DIR / "support3_panel_ecdf.png")
+    thresholds = save_threshold_bar_plot(panel_df, OUT_DIR / "support3_panel_thresholds.png")
+
+    query_curves.to_csv(OUT_DIR / "support3_query_density_curves.csv", index=False)
+    panel_curves.to_csv(OUT_DIR / "support3_panel_density_curves.csv", index=False)
+    ecdf.to_csv(OUT_DIR / "support3_panel_ecdf.csv", index=False)
+    thresholds.to_csv(OUT_DIR / "support3_panel_thresholds.csv", index=False)
+    merged.to_csv(OUT_DIR / "query_support3_rows.csv", index=False)
+    panel_df.to_csv(OUT_DIR / "panel_support3_scores.csv", index=False)
+    with (OUT_DIR / "summary_metrics.json").open("w", encoding="utf-8") as handle:
+        json.dump(build_summary(merged, panel_df), handle, indent=2)
+
+
+if __name__ == "__main__":
+    main()

evaluation/query_family/subgroup/support3_analysis/panel_support3_scores.csv

@@ -0,0 +1,838 @@
+dataset_prefix,dataset_id,model_id,subitem_id,support3_score
+c,c10,arf,internal_profile_stability,0.66
+c,c10,arf,subgroup_size_stability,0.415543
+c,c10,bayesnet,internal_profile_stability,0.333333
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evaluation/query_family/subgroup/support3_analysis/summary_metrics.json

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