ely-eye/bench/run.py

"""Run the retrieval ablation over the frozen corpus and write the baseline report.

Five arms, one database: sparse only, dense only, graph only, the product's weighted
fusion, and fusion plus a cross-encoder reranker. Every number in the report is either
measured or explicitly marked as chance.

Run: python -m bench.run [--arms bm25_only] [--limit 200] [--no-live] [--out bench/reports]
"""

from __future__ import annotations

import argparse
import json
import os
import platform
import subprocess
from collections.abc import Sequence
from dataclasses import dataclass
from datetime import UTC, datetime
from pathlib import Path
from typing import Any

from bench.metrics import aggregate, chance_recall_at_k, score_query
from bench.sets_common import MODALITIES, SETS, read_jsonl, sha256_file
from bench.systems import (
    ARMS,
    DENSE_MODEL,
    MODELS,
    RERANK_MODEL,
    ArmRunner,
    Atom,
    arm_needs,
    live_product_rankings,
    load_atoms,
)

ROOT = SETS.parent
REPORTS = ROOT / "reports"
DB_HOME = ROOT / "runs" / "baseline"
LIVE_QUERIES = 120
SCALE_SIZES = (500, 2_000, 10_000, 50_000)


@dataclass(frozen=True)
class Query:
    query_id: str
    text: str
    modality: str
    gold: frozenset[str]
    holder: str | None
    answerable: bool
    as_of: str | None


def load_queries(modalities: Sequence[str] = MODALITIES) -> list[Query]:
    """Every query the suite froze, in file order."""
    queries: list[Query] = []
    for modality in modalities:
        path = SETS / f"{modality}.jsonl"
        if not path.is_file():
            raise SystemExit(f"{path}: missing set, run python -m bench.build_sets first")
        for record in read_jsonl(path):
            gold = frozenset(hit["source_id"] for hit in record["gold"])
            answerable = not bool(record["unanswerable"])
            if answerable != bool(gold):
                raise SystemExit(
                    f"{path}: {record['query_id']} carries {len(gold)} gold hits but "
                    f"unanswerable={record['unanswerable']}"
                )
            queries.append(
                Query(
                    query_id=record["query_id"],
                    text=record["question"],
                    modality=modality,
                    gold=gold,
                    holder=record["scope"]["holder"],
                    answerable=answerable,
                    as_of=record.get("as_of"),
                )
            )
    if not queries:
        raise SystemExit("no queries loaded")
    return queries


def holder_index(modalities: Sequence[str] = MODALITIES) -> dict[str, str]:
    """source_id -> holder, from the corpus side of the suite."""
    holders: dict[str, str] = {}
    for modality in modalities:
        path = SETS / "corpus" / f"{modality}.jsonl"
        if not path.is_file():
            raise SystemExit(f"{path}: missing corpus index, run python -m bench.build_sets first")
        for record in read_jsonl(path):
            holders[record["source_id"]] = record["holder"]
    return holders


def chance_row(queries: Sequence[Query], corpus_sources: int) -> dict[str, float]:
    """What a random ranking would score on the same queries."""
    answerable = [query for query in queries if query.answerable]
    if not answerable:
        raise SystemExit("chance needs answerable queries")
    return {
        "corpus_sources": corpus_sources,
        "recall_at_1": sum(chance_recall_at_k(len(query.gold), corpus_sources, 1) for query in answerable) / len(answerable),
        "recall_at_5": sum(chance_recall_at_k(len(query.gold), corpus_sources, 5) for query in answerable) / len(answerable),
        "recall_at_10": sum(chance_recall_at_k(len(query.gold), corpus_sources, 10) for query in answerable) / len(answerable),
    }


def scale_curve(queries: Sequence[Query], atoms: Sequence[Atom], arms: Sequence[str]) -> list[dict[str, Any]]:
    """Latency of each arm as the corpus grows, measured on nested source prefixes."""
    sources = sorted({atom.source_id for atom in atoms})
    rows: list[dict[str, Any]] = []
    sample = [query for query in queries if query.answerable][:40]
    for size in SCALE_SIZES:
        keep = set(sources[:size])
        if not keep:
            raise SystemExit(f"scale size {size} kept no sources")
        subset = [atom for atom in atoms if atom.source_id in keep]
        probe = [query for query in sample if query.gold & keep]
        if not probe:
            continue
        row: dict[str, Any] = {"sources": len(keep), "atoms": len(subset), "queries": len(probe)}
        for arm in arms:
            if not arm_needs(arm, MODELS):
                continue
            runner = ArmRunner(arm, subset, MODELS)
            for query in probe:
                runner.rank(query.text)
            row[arm] = {"p50": runner.percentile(0.50), "p95": runner.percentile(0.95)}
        rows.append(row)
        print(f"scale {len(keep):6d} sources: " + " ".join(f"{arm}={row[arm]['p50']:.3f}s" for arm in arms if arm in row), flush=True)
    return rows


def live_check(queries: Sequence[Query], fast_rankings: dict[str, list[str]], top_k: int = 10) -> dict[str, Any]:
    """Compare the unmodified RetrievalService with the fast BM25 path."""
    probe = [query for query in queries if query.answerable]
    stride = max(1, len(probe) // LIVE_QUERIES)
    chosen = probe[::stride][:LIVE_QUERIES]
    if not chosen:
        raise SystemExit("live check needs answerable queries")
    rankings, timings = live_product_rankings(DB_HOME, [query.text for query in chosen], top_k)
    overlaps = [
        len(set(fast_rankings[query.query_id]) & set(ranking)) / top_k
        for query, ranking in zip(chosen, rankings, strict=True)
        if fast_rankings.get(query.query_id) is not None
    ]
    if not overlaps:
        raise SystemExit("live check found no fast rankings to compare against")
    return {
        "queries": len(chosen),
        "p50": sorted(timings)[len(timings) // 2],
        "mean": sum(timings) / len(timings),
        "max": max(timings),
        "top10_overlap_with_bm25_only": sum(overlaps) / len(overlaps),
        "identical_top10": sum(1 for overlap in overlaps if overlap == 1.0) / len(overlaps),
    }


def git_commit() -> str:
    result = subprocess.run(
        ["git", "rev-parse", "HEAD"], cwd=ROOT, capture_output=True, text=True, check=True, timeout=30
    )
    commit = result.stdout.strip()
    if len(commit) != 40:
        raise SystemExit(f"git rev-parse HEAD returned {commit!r}, expected a 40 character sha")
    return commit


def meta(corpus_sources: int, queries: Sequence[Query], limit: int | None = None) -> dict[str, Any]:
    manifest_path = SETS / "manifest.json"
    try:
        manifest = json.loads(manifest_path.read_text(encoding="utf-8"))
    except (OSError, json.JSONDecodeError) as error:
        raise SystemExit(f"{manifest_path}: cannot read the frozen set manifest: {error}") from error
    return {
        "generated_at": datetime.now(UTC).isoformat(timespec="seconds"),
        "git_commit": git_commit(),
        "python": platform.python_version(),
        "machine": {"system": platform.system(), "release": platform.release(), "cpus": os.cpu_count()},
        "corpus_manifest_sha256": manifest.get("corpus_manifest_sha256"),
        "sets": {name: sha256_file(SETS / f"{name}.jsonl") for name in MODALITIES},
        "queries": len(queries),
        "answerable": sum(1 for query in queries if query.answerable),
        "limit_per_modality": limit,
        "corpus_sources": corpus_sources,
        "models": {"dense": DENSE_MODEL, "rerank": RERANK_MODEL},
    }


def write_markdown(report: dict[str, Any], path: Path) -> None:
    """Human-readable twin of the JSON report."""
    lines = [
        "# BASELINE: what the product retrieves today",
        "",
        f"- generated {report['meta']['generated_at']} at commit {report['meta']['git_commit'][:12]}",
        (
            f"- corpus {report['meta']['corpus_sources']} sources, {report['meta']['queries']} queries "
            f"({report['meta']['answerable']} answerable)"
        ),
        f"- models: dense `{report['meta']['models']['dense']}`, rerank `{report['meta']['models']['rerank']}`",
        "",
        "| arm | recall@1 | recall@5 | recall@10 | nDCG@10 | MRR | hit@10 (95% lower) | p50 s | p95 s |",
        "|---|---|---|---|---|---|---|---|---|",
    ]
    chance = report["chance"]
    lines.append(
        f"| chance (random ranking) | {chance['recall_at_1']:.3f} | {chance['recall_at_5']:.3f} "
        f"| {chance['recall_at_10']:.3f} | - | - | - | - | - |"
    )
    for arm in report["arms"]:
        overall = arm["overall"]
        latency = arm["latency_seconds"]
        lines.append(
            f"| {arm['arm']} | {overall['recall_at_1']:.3f} | {overall['recall_at_5']:.3f} "
            f"| {overall['recall_at_10']:.3f} | {overall['ndcg_at_10']:.3f} | {overall['mrr']:.3f} "
            f"| {overall['hit_at_10']:.3f} ({overall['hit_at_10_wilson_lower']:.3f}) "
            f"| {latency['p50']:.3f} | {latency['p95']:.3f} |"
        )
    lines += ["", "## recall@10 by modality", "", "| arm | " + " | ".join(MODALITIES) + " |", "|---|" + "---|" * len(MODALITIES)]
    for arm in report["arms"]:
        cells = [f"{arm['by_modality'][modality]['recall_at_10']:.3f}" for modality in MODALITIES]
        lines.append(f"| {arm['arm']} | " + " | ".join(cells) + " |")
    lines += ["", "## unanswerable queries (44.8% of the suite)", ""]
    for arm in report["arms"]:
        overall = arm["overall"]
        lines.append(
            f"- {arm['arm']}: abstains on {overall['abstention_rate_unanswerable']:.0%} of unanswerable queries, "
            f"and puts a wrong-holder source on top for {overall['wrong_holder_confidence_unanswerable']:.0%} of them"
        )
    if "live_product" in report:
        live = report["live_product"]
        lines += [
            "",
            "## the unmodified product path",
            "",
            (
                f"- RetrievalService.search: p50 {live['p50']:.2f}s, mean {live['mean']:.2f}s, max {live['max']:.2f}s "
                f"over {live['queries']} queries"
            ),
            (
                f"- top-10 overlap with the fast BM25 path: {live['top10_overlap_with_bm25_only']:.1%}, "
                f"identical order {live['identical_top10']:.1%}"
            ),
        ]
    if report.get("scale"):
        lines += ["", "## latency vs corpus size", "", "| sources | atoms | " + " | ".join(a for a in report["scale"][0] if a not in ("sources", "atoms", "queries")) + " |", "|---" * (3 + len(report["scale"][0]) - 3) + "|"]
        for row in report["scale"]:
            cells = [f"{row[arm]['p50']:.3f}/{row[arm]['p95']:.3f}" for arm in row if arm not in ("sources", "atoms", "queries")]
            lines.append(f"| {row['sources']} | {row['atoms']} | " + " | ".join(cells) + " |")
    lines += [
        "",
        "## reading",
        "",
        "- recall counts a source as found only when it is the source a third party marked correct.",
        "- abstention is the only honest number for unanswerable queries: a retriever that always",
        "  returns ten results never says it does not know.",
        "- chance is the floor any of these arms must beat; a number near chance is not a capability.",
    ]
    path.write_text("\n".join(lines) + "\n", encoding="utf-8")


def load_config(parser: argparse.ArgumentParser, args: argparse.Namespace, path: Path) -> None:
    """Fill in whatever the command line left at its default; an explicit flag still wins."""
    try:
        config = json.loads(path.read_text(encoding="utf-8"))
    except (OSError, json.JSONDecodeError) as error:
        raise SystemExit(f"{path}: cannot read the run config ({error})") from error
    if not isinstance(config, dict):
        raise SystemExit(f"{path}: expected a JSON object, got {type(config).__name__}")
    for key, value in config.items():
        if key not in vars(args):
            raise SystemExit(f"{path}: unknown key {key!r}, expected one of {sorted(vars(args))}")
        if getattr(args, key) == parser.get_default(key):
            setattr(args, key, value)
    unknown = [arm for arm in args.arms if arm not in ARMS]
    if unknown:
        raise SystemExit(f"{path}: unknown arm(s) {unknown}, expected one of {list(ARMS)}")


def main() -> int:
    parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
    parser.add_argument("--arms", nargs="+", choices=ARMS, default=list(ARMS))
    parser.add_argument("--modality", action="append", choices=MODALITIES, default=None)
    parser.add_argument("--limit", type=int, default=None, help="score at most this many queries per modality")
    parser.add_argument("--no-live", action="store_true", help="skip the unmodified RetrievalService path")
    parser.add_argument("--no-scale", action="store_true", help="skip the latency/scale curve")
    parser.add_argument("--out", type=Path, default=REPORTS)
    parser.add_argument("--config", type=Path, help="JSON defaults for these flags (bench/configs/ablation.json)")
    args = parser.parse_args()
    if args.config:
        load_config(parser, args, args.config)

    modalities = args.modality or list(MODALITIES)
    queries = load_queries(modalities)
    if args.limit:
        capped: list[Query] = []
        for modality in modalities:
            picked = [query for query in queries if query.modality == modality][: args.limit]
            capped.extend(picked)
        queries = capped
    atoms = load_atoms(DB_HOME / "data" / "ely_eye.sqlite")
    holders = holder_index(modalities)
    corpus_sources = len({atom.source_id for atom in atoms})
    print(f"{len(queries)} queries, {len(atoms)} atoms, {corpus_sources} sources", flush=True)

    report: dict[str, Any] = {
        "meta": meta(corpus_sources, queries, args.limit),
        "chance": chance_row(queries, corpus_sources),
    }
    arms: list[dict[str, Any]] = []
    fast_rankings: dict[str, list[str]] = {}
    for arm in args.arms:
        print(f"== {arm}", flush=True)
        runner = ArmRunner(arm, atoms, MODELS)
        scores = []
        for query in queries:
            ranked = runner.rank(query.text)
            scores.append(score_query(query.query_id, ranked, set(query.gold), holder=query.holder, holder_of=holders))
            if arm == "bm25_only":
                fast_rankings[query.query_id] = ranked
        summary: dict[str, Any] = {"arm": arm, "overall": aggregate(scores)}
        summary["by_modality"] = {
            modality: aggregate([s for s, q in zip(scores, queries, strict=True) if q.modality == modality])
            for modality in sorted({q.modality for q in queries})
        }
        summary["latency_seconds"] = {
            "queries": len(runner.timings),
            "mean": sum(runner.timings) / len(runner.timings),
            "p50": runner.percentile(0.50),
            "p95": runner.percentile(0.95),
            "max": max(runner.timings),
        }
        arms.append(summary)
        print(
            f"   recall@10 {summary['overall']['recall_at_10']:.3f} "
            f"hit@10 {summary['overall']['hit_at_10']:.3f} "
            f"(95% lower {summary['overall']['hit_at_10_wilson_lower']:.3f}) "
            f"abstain {summary['overall']['abstention_rate_unanswerable']:.0%} "
            f"p95 {summary['latency_seconds']['p95']:.3f}s",
            flush=True,
        )
    report["arms"] = arms
    if not args.no_live:
        report["live_product"] = live_check(queries, fast_rankings)
    if not args.no_scale:
        report["scale"] = scale_curve(queries, atoms, [arm for arm in args.arms if arm in ("bm25_only", "dense_only")])

    args.out.mkdir(parents=True, exist_ok=True)
    (args.out / "baseline.json").write_text(json.dumps(report, indent=1, sort_keys=True), encoding="utf-8")
    write_markdown(report, args.out / "BASELINE.md")
    print(f"== wrote {args.out / 'baseline.json'} and {args.out / 'BASELINE.md'}", flush=True)
    return 0


if __name__ == "__main__":
    raise SystemExit(main())