Upload subgroup support3-vs-profile analysis

evaluation/query_family/subgroup/support3_vs_profile_analysis/README.md

@@ -0,0 +1,20 @@
+# Support3 vs. Profile Analysis
+
+This folder builds a support-oriented version of the original
+`support_vs_profile_query_density.png` figure.
+
+Instead of the canonical subgroup `query_score`, the x-axis uses:
+
+- `support3_score = mean(key_set_score, row_count_score, column_score)`
+
+This makes the figure align more directly with the paper story that current
+generators are better at preserving the subgroup scaffold/support than the
+within-group analytical profile.
+
+Files:
+
+- `build_support3_vs_profile_analysis.py`
+- `support3_vs_profile_query_density.png`
+- `support3_vs_profile_query_density.tex`
+- `support3_density_curves.csv`
+- `summary_metrics.json`

evaluation/query_family/subgroup/support3_vs_profile_analysis/build_support3_vs_profile_analysis.py

@@ -0,0 +1,292 @@
+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
+    gaussian_kde = None
+
+
+INTERNAL_COLOR = "#AA3377"
+SIZE_COLOR = "#009988"
+RUN_TAG = "v2_keyset_cts_48_20260504_2350"
+
+
+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_4": float((series <= 0.4).mean()),
+        "share_ge_0_8": float((series >= 0.8).mean()),
+    }
+
+
+def load_query_rows(data_dir: Path) -> pd.DataFrame:
+    df = pd.read_csv(data_dir / "subgroup_query_rows.csv")
+    df = df[df["model_id"] != "real"].copy()
+    df["query_score"] = pd.to_numeric(df["query_score"], errors="coerce")
+    return df
+
+
+def load_query_details(run_dir: Path, 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 = run_dir / "datasets" / 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"],
+                        "strict_set_score": details.get("strict_set_score", details.get("set_score")),
+                        "key_set_score": details.get("key_set_score"),
+                        "profile_score": details.get("profile_score"),
+                        "row_count_score": details.get("row_count_score"),
+                        "column_score": details.get("column_score"),
+                    }
+                )
+
+    return pd.DataFrame(rows)
+
+
+def add_support_scores(df: pd.DataFrame) -> pd.DataFrame:
+    out = df.copy()
+    for col in [
+        "strict_set_score",
+        "key_set_score",
+        "profile_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 build_plot(curves: pd.DataFrame, stats: dict[str, dict[str, float]], out_dir: Path) -> None:
+    plt.style.use("seaborn-v0_8-whitegrid")
+    fig, ax = plt.subplots(figsize=(8.8, 5.4))
+
+    ax.fill_between(
+        curves["score"],
+        curves["internal_support3_density"],
+        color=INTERNAL_COLOR,
+        alpha=0.14,
+    )
+    ax.plot(
+        curves["score"],
+        curves["internal_support3_density"],
+        color=INTERNAL_COLOR,
+        linewidth=2.5,
+        label="Internal profile stability",
+    )
+    ax.fill_between(
+        curves["score"],
+        curves["size_support3_density"],
+        color=SIZE_COLOR,
+        alpha=0.14,
+    )
+    ax.plot(
+        curves["score"],
+        curves["size_support3_density"],
+        color=SIZE_COLOR,
+        linewidth=2.5,
+        label="Subgroup size stability",
+    )
+
+    mean_internal = stats["internal_profile"]["mean"]
+    mean_size = stats["subgroup_size"]["mean"]
+    ax.axvline(mean_internal, color=INTERNAL_COLOR, linestyle="--", linewidth=1.2, alpha=0.85)
+    ax.axvline(mean_size, color=SIZE_COLOR, linestyle="--", linewidth=1.2, alpha=0.85)
+
+    ax.set_xlim(0.0, 1.0)
+    ax.set_xlabel("Query-level support3 score")
+    ax.set_ylabel("Relative density (area = 1)")
+    ax.set_title("Support-oriented subgroup preservation at the query level")
+    ax.text(
+        0.015,
+        0.97,
+        (
+            f"Queries: internal = {stats['internal_profile']['count']}, size = {stats['subgroup_size']['count']}\n"
+            f"Means: internal = {mean_internal:.3f}, size = {mean_size:.3f}\n"
+            f"Medians: internal = {stats['internal_profile']['median']:.3f}, "
+            f"size = {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 / "support3_vs_profile_query_density.png", dpi=240)
+    plt.close(fig)
+
+
+def write_tex(curves: pd.DataFrame, stats: dict[str, dict[str, float]], out_dir: Path) -> None:
+    ymax = max(curves["internal_support3_density"].max(), curves["size_support3_density"].max()) * 1.05
+    mean_internal = stats["internal_profile"]["mean"]
+    mean_size = stats["subgroup_size"]["mean"]
+    internal_count = stats["internal_profile"]["count"]
+    size_count = stats["subgroup_size"]["count"]
+    internal_median = stats["internal_profile"]["median"]
+    size_median = stats["subgroup_size"]["median"]
+
+    tex = rf"""\documentclass[tikz,border=4pt]{{standalone}}
+\usepackage{{pgfplots}}
+\usepackage{{pgfplotstable}}
+\pgfplotsset{{compat=1.18}}
+\definecolor{{internalcolor}}{{HTML}}{{AA3377}}
+\definecolor{{sizecolor}}{{HTML}}{{009988}}
+\begin{{document}}
+\begin{{tikzpicture}}
+\begin{{axis}}[
+width=14.8cm,
+height=8.7cm,
+xmin=0.0, xmax=1.0,
+ymin=0.0, ymax={ymax:.4f},
+xlabel={{Query-level support3 score}},
+ylabel={{Relative density (area = 1)}},
+title={{Support-oriented subgroup preservation at the query level}},
+ymajorgrids,
+grid style={{draw=gray!20}},
+major grid style={{draw=gray!30}},
+axis line style={{draw=black!70}},
+tick style={{draw=black!70}},
+legend style={{draw=none, fill=none, font=\small, at={{(0.98,0.98)}}, anchor=north east}},
+]
+\addplot[draw=none, fill=internalcolor, fill opacity=0.14]
+table[x=score, y=internal_support3_density, col sep=comma]{{support3_density_curves.csv}} \closedcycle;
+\addplot[line width=1.9pt, color=internalcolor]
+table[x=score, y=internal_support3_density, col sep=comma]{{support3_density_curves.csv}};
+\addlegendentry{{Internal profile stability}}
+\addplot[draw=none, fill=sizecolor, fill opacity=0.14]
+table[x=score, y=size_support3_density, col sep=comma]{{support3_density_curves.csv}} \closedcycle;
+\addplot[line width=1.9pt, color=sizecolor]
+table[x=score, y=size_support3_density, col sep=comma]{{support3_density_curves.csv}};
+\addlegendentry{{Subgroup size stability}}
+\addplot[dashed, line width=1.0pt, color=internalcolor, opacity=0.85]
+coordinates {{({mean_internal:.6f},0) ({mean_internal:.6f},{ymax:.4f})}};
+\addplot[dashed, line width=1.0pt, color=sizecolor, opacity=0.85]
+coordinates {{({mean_size:.6f},0) ({mean_size:.6f},{ymax:.4f})}};
+\node[
+anchor=north west,
+draw=gray!55,
+fill=white,
+rounded corners=2pt,
+fill opacity=0.96,
+text opacity=1,
+align=left,
+font=\small
+] at (axis description cs:0.015,0.97) {{
+Queries: internal = {internal_count}, size = {size_count}\\
+Means: internal = {mean_internal:.3f}, size = {mean_size:.3f}\\
+Medians: internal = {internal_median:.3f}, size = {size_median:.3f}
+}};
+\end{{axis}}
+\end{{tikzpicture}}
+\end{{document}}
+"""
+    (out_dir / "support3_vs_profile_query_density.tex").write_text(tex, encoding="utf-8")
+
+
+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"
+    run_dir = project_root / "Evaluation" / "analysis" / "runs" / RUN_TAG
+
+    query_rows = load_query_rows(subgroup_root / "data")
+    detail_rows = load_query_details(run_dir, query_rows)
+    merged = query_rows.merge(
+        detail_rows,
+        on=["dataset_id", "model_id", "asset_key", "query_id"],
+        how="left",
+    )
+    merged = add_support_scores(merged)
+
+    internal = merged.loc[
+        merged["subitem_id"] == "internal_profile_stability", "support3_score"
+    ].astype(float)
+    size = merged.loc[
+        merged["subitem_id"] == "subgroup_size_stability", "support3_score"
+    ].astype(float)
+
+    stats = {
+        "internal_profile": _branch_summary(internal),
+        "subgroup_size": _branch_summary(size),
+    }
+
+    grid = np.linspace(0.0, 1.0, 500)
+    curves = pd.DataFrame(
+        {
+            "score": grid,
+            "internal_support3_density": _density_curve(internal.to_numpy(), grid),
+            "size_support3_density": _density_curve(size.to_numpy(), grid),
+        }
+    )
+    curves.to_csv(out_dir / "support3_density_curves.csv", index=False)
+    (out_dir / "summary_metrics.json").write_text(
+        json.dumps(
+            {
+                "run_tag": RUN_TAG,
+                "metric": "support3_score",
+                "definition": "mean(key_set_score, row_count_score, column_score)",
+                "row_level": stats,
+            },
+            indent=2,
+            ensure_ascii=False,
+        ),
+        encoding="utf-8",
+    )
+
+    build_plot(curves, stats, out_dir)
+    write_tex(curves, stats, out_dir)
+
+
+if __name__ == "__main__":
+    main()

evaluation/query_family/subgroup/support3_vs_profile_analysis/summary_metrics.json

@@ -0,0 +1,25 @@
+{
+  "run_tag": "v2_keyset_cts_48_20260504_2350",
+  "metric": "support3_score",
+  "definition": "mean(key_set_score, row_count_score, column_score)",
+  "row_level": {
+    "internal_profile": {
+      "count": 13332,
+      "mean": 0.7770301021787321,
+      "median": 0.8888888888888888,
+      "p25": 0.625,
+      "p75": 1.0,
+      "share_le_0_4": 0.14303930393039305,
+      "share_ge_0_8": 0.5377287728772877
+    },
+    "subgroup_size": {
+      "count": 9352,
+      "mean": 0.8184448182874395,
+      "median": 1.0,
+      "p25": 0.6666666666666666,
+      "p75": 1.0,
+      "share_le_0_4": 0.1292771599657827,
+      "share_ge_0_8": 0.6319503849443969
+    }
+  }
+}

evaluation/query_family/subgroup/support3_vs_profile_analysis/support3_density_curves.csv

@@ -0,0 +1,501 @@
+score,internal_support3_density,size_support3_density
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evaluation/query_family/subgroup/support3_vs_profile_analysis/support3_vs_profile_query_density.tex

@@ -0,0 +1,54 @@
+\documentclass[tikz,border=4pt]{standalone}
+\usepackage{pgfplots}
+\usepackage{pgfplotstable}
+\pgfplotsset{compat=1.18}
+\definecolor{internalcolor}{HTML}{AA3377}
+\definecolor{sizecolor}{HTML}{009988}
+\begin{document}
+\begin{tikzpicture}
+\begin{axis}[
+width=14.8cm,
+height=8.7cm,
+xmin=0.0, xmax=1.0,
+ymin=0.0, ymax=8.0932,
+xlabel={Query-level support3 score},
+ylabel={Relative density (area = 1)},
+title={Support-oriented subgroup preservation at the query level},
+ymajorgrids,
+grid style={draw=gray!20},
+major grid style={draw=gray!30},
+axis line style={draw=black!70},
+tick style={draw=black!70},
+legend style={draw=none, fill=none, font=\small, at={(0.98,0.98)}, anchor=north east},
+]
+\addplot[draw=none, fill=internalcolor, fill opacity=0.14]
+table[x=score, y=internal_support3_density, col sep=comma]{support3_density_curves.csv} \closedcycle;
+\addplot[line width=1.9pt, color=internalcolor]
+table[x=score, y=internal_support3_density, col sep=comma]{support3_density_curves.csv};
+\addlegendentry{Internal profile stability}
+\addplot[draw=none, fill=sizecolor, fill opacity=0.14]
+table[x=score, y=size_support3_density, col sep=comma]{support3_density_curves.csv} \closedcycle;
+\addplot[line width=1.9pt, color=sizecolor]
+table[x=score, y=size_support3_density, col sep=comma]{support3_density_curves.csv};
+\addlegendentry{Subgroup size stability}
+\addplot[dashed, line width=1.0pt, color=internalcolor, opacity=0.85]
+coordinates {(0.777030,0) (0.777030,8.0932)};
+\addplot[dashed, line width=1.0pt, color=sizecolor, opacity=0.85]
+coordinates {(0.818445,0) (0.818445,8.0932)};
+\node[
+anchor=north west,
+draw=gray!55,
+fill=white,
+rounded corners=2pt,
+fill opacity=0.96,
+text opacity=1,
+align=left,
+font=\small
+] at (axis description cs:0.015,0.97) {
+Queries: internal = 13332, size = 9352\\
+Means: internal = 0.777, size = 0.818\\
+Medians: internal = 0.889, size = 1.000
+};
+\end{axis}
+\end{tikzpicture}
+\end{document}