Upload subgroup key-set analysis

evaluation/query_family/subgroup/key_set_analysis/build_key_set_query_density.py

@@ -0,0 +1,149 @@
+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"
+    / "key_set_analysis"
+)
+
+INTERNAL_ID = "internal_profile_stability"
+SIZE_ID = "subgroup_size_stability"
+PALETTE = {
+    INTERNAL_ID: "#8b2f5d",
+    SIZE_ID: "#1f7a5c",
+}
+
+
+def load_rows() -> pd.DataFrame:
+    df = pd.read_csv(SUBGROUP_DATA_PATH)
+    return df[df["model_id"] != "real"].copy()
+
+
+def load_key_scores(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"),
+                    }
+                )
+    return pd.DataFrame(rows)
+
+
+def kde_curve(values: np.ndarray, grid: np.ndarray, bandwidth: float = 0.04) -> 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 main() -> None:
+    OUT_DIR.mkdir(parents=True, exist_ok=True)
+    subgroup = load_rows()
+    key_scores = load_key_scores(subgroup)
+    merged = subgroup.merge(key_scores, on=["dataset_id", "model_id", "asset_key", "query_id"], how="left")
+    merged["key_set_score"] = pd.to_numeric(merged["key_set_score"], errors="coerce")
+
+    grid = np.linspace(0, 1, 500)
+    curve_rows: list[dict[str, float | str]] = []
+
+    plt.figure(figsize=(8.6, 5.2))
+    summary_lines: list[str] = []
+    for subitem_id, label in [
+        (INTERNAL_ID, "Internal profile"),
+        (SIZE_ID, "Subgroup size"),
+    ]:
+        values = merged.loc[merged["subitem_id"] == subitem_id, "key_set_score"].dropna().to_numpy()
+        density = kde_curve(values, grid)
+        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])
+        summary_lines.append(
+            f"{label}: mean {values.mean():.3f}, median {np.median(values):.3f}, n={len(values)}"
+        )
+        for x, y in zip(grid, density):
+            curve_rows.append({"subitem_id": subitem_id, "score": float(x), "density": float(y)})
+
+    plt.xlim(0, 1)
+    plt.xlabel("Query-level key_set_score")
+    plt.ylabel("Relative density (area = 1)")
+    plt.title("Support vs. profile at the query level using only key_set_score")
+    plt.legend(frameon=False)
+    plt.text(
+        0.985,
+        0.97,
+        "\n".join(summary_lines),
+        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_DIR / "key_set_query_density.png", dpi=220, bbox_inches="tight")
+    plt.close()
+
+    pd.DataFrame(curve_rows).to_csv(OUT_DIR / "key_set_query_density_curves.csv", index=False)
+    summary = {
+        "internal_mean": round(
+            float(merged.loc[merged["subitem_id"] == INTERNAL_ID, "key_set_score"].mean()), 6
+        ),
+        "size_mean": round(float(merged.loc[merged["subitem_id"] == SIZE_ID, "key_set_score"].mean()), 6),
+        "internal_median": round(
+            float(merged.loc[merged["subitem_id"] == INTERNAL_ID, "key_set_score"].median()), 6
+        ),
+        "size_median": round(float(merged.loc[merged["subitem_id"] == SIZE_ID, "key_set_score"].median()), 6),
+        "internal_n": int(merged.loc[merged["subitem_id"] == INTERNAL_ID, "key_set_score"].notna().sum()),
+        "size_n": int(merged.loc[merged["subitem_id"] == SIZE_ID, "key_set_score"].notna().sum()),
+    }
+    with (OUT_DIR / "summary_metrics.json").open("w", encoding="utf-8") as handle:
+        json.dump(summary, handle, indent=2)
+
+
+if __name__ == "__main__":
+    main()

evaluation/query_family/subgroup/key_set_analysis/key_set_query_density_curves.csv

@@ -0,0 +1,1001 @@
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evaluation/query_family/subgroup/key_set_analysis/summary_metrics.json

@@ -0,0 +1,8 @@
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