scripts/run_release_evaluation.py

#!/usr/bin/env python3
from __future__ import annotations

import argparse
import csv
import json
import subprocess
import sys
from pathlib import Path
from typing import Any

import pandas as pd


DISPLAY_NAME = {
    "continuous": "Continuous reference",
    "standard_rvq_8bit": "standard RVQ 8-bit",
    "pq_8bit": "PQ 8-bit",
    "opq_8bit": "OPQ 8-bit",
    "metadata_basic": "Metadata basic",
    "metadata_calendar": "Metadata calendar",
    "random_permuted_continuous": "Random-permuted continuous",
}

PUBLIC_METHODS = [
    "continuous",
    "standard_rvq_8bit",
    "pq_8bit",
    "opq_8bit",
    "metadata_basic",
    "metadata_calendar",
    "random_permuted_continuous",
]


def parse_args() -> argparse.Namespace:
    parser = argparse.ArgumentParser(
        description="Recompute the released CB-Telemetry retrieval and shortcut-control tables."
    )
    parser.add_argument("--root", default=".", help="CB-Telemetry dataset root.")
    parser.add_argument("--output-dir", default="evaluation_runs/release_retrieval", help="Output directory under --root.")
    parser.add_argument("--bootstrap-samples", type=int, default=2000, help="Bootstrap resamples.")
    parser.add_argument("--bootstrap-seed", type=int, default=42, help="Bootstrap random seed.")
    parser.add_argument("--random-seed", type=int, default=42, help="Random-permuted control seed.")
    parser.add_argument("--include-gap2", action="store_true", help="Also run the supplemental gap=2 retrieval controls.")
    parser.add_argument("--tolerance", type=float, default=5e-3, help="Rounded-table comparison tolerance.")
    return parser.parse_args()


def resolve(root: Path, raw_path: str) -> Path:
    path = Path(raw_path).expanduser()
    if not path.is_absolute():
        path = (root / path).resolve()
    return path


def release_path(root: Path, path: Path) -> str:
    try:
        return path.resolve().relative_to(root).as_posix()
    except ValueError:
        return path.as_posix()


def track_feature_table(track: str) -> str:
    return "features/feature_table_default.csv.gz" if track == "default" else "features/feature_table_strict_clean.csv.gz"


def bottleneck_representations(root: Path, track: str) -> list[str]:
    result = []
    for method in ["standard_rvq_8bit", "pq_8bit", "opq_8bit"]:
        rel = f"features/bottlenecks/{track}/{method}_feature_table.csv.gz"
        if (root / rel).exists():
            result.append(f"{method}={rel}")
    return result


def run_command(cmd: list[str]) -> None:
    subprocess.run(cmd, check=True)


def run_retrieval_suite(
    root: Path,
    output_dir: Path,
    bootstrap_samples: int,
    bootstrap_seed: int,
    random_seed: int,
    include_gap2: bool,
) -> list[dict[str, Any]]:
    script = Path(__file__).resolve().parent / "run_retrieval_eval.py"
    run_rows: list[dict[str, Any]] = []
    gaps = [1, 2] if include_gap2 else [1]
    for track in ["default", "strict_clean"]:
        feature_table = track_feature_table(track)
        representations = [f"continuous={feature_table}", *bottleneck_representations(root, track)]
        for scope_label, archive_scope in [("global", "global"), ("same-archive", "same_archive_only")]:
            for gap in gaps:
                run_dir = output_dir / track / f"{archive_scope}_gap{gap}"
                cmd = [
                    sys.executable,
                    script.as_posix(),
                    "--root",
                    root.as_posix(),
                    "--feature-table",
                    feature_table,
                    "--output-dir",
                    run_dir.as_posix(),
                    "--archive-scope",
                    archive_scope,
                    "--max-slot-gap",
                    str(gap),
                    "--bootstrap-samples",
                    str(bootstrap_samples),
                    "--bootstrap-seed",
                    str(bootstrap_seed),
                    "--random-seed",
                    str(random_seed),
                    "--include-metadata-controls",
                ]
                for item in representations:
                    cmd.extend(["--representation", item])
                run_command(cmd)
                run_rows.append(
                    {
                        "track": track,
                        "scope": scope_label,
                        "gap": gap,
                        "archive_scope": archive_scope,
                        "summary_json_path": (run_dir / "summary.json").as_posix(),
                        "summary_json": release_path(root, run_dir / "summary.json"),
                    }
                )
    return run_rows


def load_json(path: Path) -> dict[str, Any]:
    return json.loads(path.read_text(encoding="utf-8"))


def build_overview(run_rows: list[dict[str, Any]]) -> pd.DataFrame:
    rows: list[dict[str, Any]] = []
    for run in run_rows:
        summary = load_json(Path(run["summary_json_path"]))
        aggregate_df = pd.DataFrame(summary["aggregate_rows"]).set_index("method")
        bootstrap_df = pd.DataFrame(summary["bootstrap_rows"])
        for _, boot in bootstrap_df.iterrows():
            method = str(boot["method"])
            aggregate = aggregate_df.loc[method]
            rows.append(
                {
                    "track": run["track"],
                    "scope": run["scope"],
                    "gap": int(run["gap"]),
                    "archive_scope": run["archive_scope"],
                    "method": method,
                    "display_name": DISPLAY_NAME.get(method, method),
                    "bootstrap_unit": str(boot["bootstrap_unit"]),
                    "top1": float(aggregate["mean_top1_hit_rate"]),
                    "top1_ci_low": float(boot["top1_hit_rate_ci_low"]),
                    "top1_ci_high": float(boot["top1_hit_rate_ci_high"]),
                    "mrr": float(aggregate["mean_mrr"]),
                    "mrr_ci_low": float(boot["mrr_ci_low"]),
                    "mrr_ci_high": float(boot["mrr_ci_high"]),
                    "top5": float(aggregate["mean_top5_hit_rate"]),
                    "top5_ci_low": float(boot["top5_hit_rate_ci_low"]),
                    "top5_ci_high": float(boot["top5_hit_rate_ci_high"]),
                    "mean_candidate_size": float(aggregate["mean_candidate_size"]),
                    "mean_chance_top1": float(aggregate["mean_chance_top1"]),
                    "summary_json": str(run["summary_json"]),
                }
            )
    return pd.DataFrame(rows).sort_values(["track", "scope", "gap", "bootstrap_unit", "method"]).reset_index(drop=True)


def date_row(df: pd.DataFrame, track: str, method: str, scope: str, gap: int) -> pd.Series:
    rows = df[
        (df["track"] == track)
        & (df["method"] == method)
        & (df["scope"] == scope)
        & (df["gap"] == gap)
        & (df["bootstrap_unit"] == "date")
    ]
    if rows.empty:
        raise KeyError(f"Missing row: track={track}, method={method}, scope={scope}, gap={gap}")
    return rows.iloc[0]


def ci_text(row: pd.Series, metric: str) -> str:
    return f"{float(row[metric]):.4f} [{float(row[f'{metric}_ci_low']):.4f}, {float(row[f'{metric}_ci_high']):.4f}]"


def build_table3_recomputed(overview: pd.DataFrame) -> pd.DataFrame:
    rows = []
    for method in PUBLIC_METHODS:
        global_row = date_row(overview, "default", method, "global", 1)
        same_row = date_row(overview, "default", method, "same-archive", 1)
        rows.append(
            {
                "method": method,
                "display_name": DISPLAY_NAME.get(method, method),
                "global_top1": round(float(global_row["top1"]), 4),
                "global_mrr": round(float(global_row["mrr"]), 4),
                "global_top5": round(float(global_row["top5"]), 4),
                "same_archive_top1": round(float(same_row["top1"]), 4),
                "same_archive_mrr": round(float(same_row["mrr"]), 4),
                "same_archive_top5": round(float(same_row["top5"]), 4),
                "global_mrr_ci": ci_text(global_row, "mrr"),
                "same_archive_mrr_ci": ci_text(same_row, "mrr"),
            }
        )
    return pd.DataFrame(rows)


def build_dual_track_recomputed(root: Path, overview: pd.DataFrame) -> pd.DataFrame:
    rows = []
    for track in ["default", "strict_clean"]:
        feature_df = pd.read_csv(root / track_feature_table(track), usecols=["date"])
        for scope_label, archive_scope in [("global", "global"), ("same-archive", "same_archive_only")]:
            row = date_row(overview, track, "continuous", scope_label, 1)
            rows.append(
                {
                    "track": f"{track}__{archive_scope}",
                    "subset": track,
                    "archive_scope": archive_scope,
                    "rows": int(feature_df.shape[0]),
                    "date_count": int(feature_df["date"].astype(str).nunique()),
                    "top1": round(float(row["top1"]), 4),
                    "mrr": round(float(row["mrr"]), 4),
                    "top5": round(float(row["top5"]), 4),
                    "chance_top1": round(float(row["mean_chance_top1"]), 4),
                }
            )
    return pd.DataFrame(rows)


def compare_table(
    expected_path: Path,
    actual_df: pd.DataFrame,
    key_columns: list[str],
    metric_columns: list[str],
    tolerance: float,
) -> list[dict[str, Any]]:
    mismatches: list[dict[str, Any]] = []
    if not expected_path.exists():
        return [{"table": expected_path.name, "issue": "missing_expected_table"}]
    expected_df = pd.read_csv(expected_path)
    merged = expected_df.merge(actual_df, on=key_columns, how="outer", suffixes=("_expected", "_actual"), indicator=True)
    for _, row in merged.iterrows():
        key = {column: row[column] for column in key_columns}
        if row["_merge"] != "both":
            mismatches.append({"table": expected_path.name, "key": key, "issue": str(row["_merge"])})
            continue
        for column in metric_columns:
            expected = float(row[f"{column}_expected"])
            actual = float(row[f"{column}_actual"])
            if abs(expected - actual) > tolerance:
                mismatches.append(
                    {
                        "table": expected_path.name,
                        "key": key,
                        "metric": column,
                        "expected": expected,
                        "actual": actual,
                        "abs_delta": abs(expected - actual),
                    }
                )
    return mismatches


def write_markdown(path: Path, check: dict[str, Any]) -> None:
    lines = [
        "# CB-Telemetry Release Evaluation Check",
        "",
        f"- status: `{check['status']}`",
        f"- bootstrap_samples: `{check['bootstrap_samples']}`",
        f"- overview_rows: `{check['overview_rows']}`",
        f"- mismatches: `{len(check['mismatches'])}`",
        "",
        "## Mismatches",
        "",
    ]
    if check["mismatches"]:
        for item in check["mismatches"]:
            lines.append(f"- `{item}`")
    else:
        lines.append("- none")
    path.write_text("\n".join(lines) + "\n", encoding="utf-8")


def main() -> None:
    args = parse_args()
    root = Path(args.root).expanduser().resolve()
    output_dir = resolve(root, args.output_dir)
    output_dir.mkdir(parents=True, exist_ok=True)

    run_rows = run_retrieval_suite(
        root=root,
        output_dir=output_dir,
        bootstrap_samples=int(args.bootstrap_samples),
        bootstrap_seed=int(args.bootstrap_seed),
        random_seed=int(args.random_seed),
        include_gap2=bool(args.include_gap2),
    )
    overview = build_overview(run_rows)
    overview_path = output_dir / "release_retrieval_overview.csv"
    overview.to_csv(overview_path, index=False)

    table3 = build_table3_recomputed(overview)
    table3_path = output_dir / "table_3_retrieval_baselines_and_controls_recomputed.csv"
    table3.to_csv(table3_path, index=False)

    dual = build_dual_track_recomputed(root, overview)
    dual_path = output_dir / "table_3_dual_track_retrieval_recomputed.csv"
    dual.to_csv(dual_path, index=False)

    mismatches: list[dict[str, Any]] = []
    mismatches.extend(
        compare_table(
            expected_path=root / "baselines" / "table_3_retrieval_baselines_and_controls.csv",
            actual_df=table3,
            key_columns=["method"],
            metric_columns=[
                "global_top1",
                "global_mrr",
                "global_top5",
                "same_archive_top1",
                "same_archive_mrr",
                "same_archive_top5",
            ],
            tolerance=float(args.tolerance),
        )
    )
    mismatches.extend(
        compare_table(
            expected_path=root / "baselines" / "table_3_dual_track_retrieval.csv",
            actual_df=dual,
            key_columns=["track"],
            metric_columns=["top1", "mrr", "top5", "chance_top1"],
            tolerance=float(args.tolerance),
        )
    )

    check = {
        "status": "pass" if not mismatches else "fail",
        "root": root.name,
        "output_dir": release_path(root, output_dir),
        "bootstrap_samples": int(args.bootstrap_samples),
        "include_gap2": bool(args.include_gap2),
        "overview_rows": int(overview.shape[0]),
        "files": {
            "overview": release_path(root, overview_path),
            "table3_recomputed": release_path(root, table3_path),
            "dual_track_recomputed": release_path(root, dual_path),
        },
        "mismatches": mismatches,
    }
    (output_dir / "release_evaluation_check.json").write_text(
        json.dumps(check, ensure_ascii=False, indent=2) + "\n",
        encoding="utf-8",
    )
    write_markdown(output_dir / "release_evaluation_check.md", check)
    print(json.dumps(check, ensure_ascii=False, indent=2))
    raise SystemExit(0 if check["status"] == "pass" else 1)


if __name__ == "__main__":
    main()