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summarize.py

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+#!/usr/bin/env python3
+"""Aggregate results/*.jsonl into a comparison table."""
+
+from __future__ import annotations
+
+import json
+import sys
+from pathlib import Path
+
+import pandas as pd
+
+sys.path.insert(0, str(Path(__file__).resolve().parent / "bench"))
+import oolong  # noqa: E402
+
+RESULTS = Path(__file__).resolve().parent / "results"
+
+# Published list prices, USD per million tokens (input, output). Cache reads bill
+# at 0.1x input and cache writes at 1.25x input.
+PRICES = {
+    "claude-haiku-4-5-20251001": (1.0, 5.0),
+    "claude-sonnet-4-6": (3.0, 15.0),
+    "claude-opus-4-8": (15.0, 75.0),
+}
+
+
+def api_cost(row, model: str) -> float:
+    """Cost from raw token counts at list prices.
+
+    The CLI's own total_cost_usd also bills a small hidden Haiku call it makes per
+    invocation, so this is the apples-to-apples number for model comparisons.
+    """
+    pin, pout = PRICES[model.replace("[1m]", "")]
+    return (
+        row.get("usage_input_tokens", 0) * pin
+        + row.get("usage_cache_creation_tokens", 0) * pin * 1.25
+        + row.get("usage_cache_read_tokens", 0) * pin * 0.1
+        + row.get("usage_output_tokens", 0) * pout
+    ) / 1e6
+
+
+def load(tag: str) -> pd.DataFrame:
+    rows = [json.loads(l) for l in (RESULTS / f"{tag}.jsonl").read_text().splitlines() if l.strip()]
+    df = pd.json_normalize(rows, sep="_")
+    meta = json.loads((RESULTS / f"{tag}.meta.json").read_text())
+    df = df.assign(tag=tag, **meta)
+    df["api_cost"] = df.apply(lambda r: api_cost(r, meta["model"]), axis=1)
+
+    # Guard the headline number against the answer extractor. `score` uses our
+    # clean_final() pre-step; `score_raw` is the official parser on the untouched
+    # response. If the two disagree by much, the gap is a parsing artifact rather
+    # than a capability difference.
+    df["score_raw"] = [
+        oolong.score_response({"answer": repr([g]), "answer_type": t}, a)["score"] if a else 0.0
+        for g, t, a in zip(df.gold, df.answer_type, df.full_answer)
+    ]
+    return df
+
+
+def main(tags: list[str]) -> None:
+    tags = tags or sorted(p.stem for p in RESULTS.glob("*.jsonl"))
+    df = pd.concat([load(t) for t in tags], ignore_index=True)
+
+    agg = df.groupby(["tag", "mode", "model", "context_len"], as_index=False).agg(
+        n=("score", "size"),
+        score=("score", "mean"),
+        score_raw=("score_raw", "mean"),
+        exact=("score", lambda s: (s == 1.0).mean()),
+        errors=("error", lambda e: e.notna().sum()),
+        cost_per_q=("api_cost", "mean"),
+        cli_cost_per_q=("usage_cost_usd", "mean"),
+        calls_per_q=("usage_calls", "mean"),
+        sec_per_q=("seconds", "mean"),
+    )
+    for c in ("score", "score_raw", "exact"):
+        agg[c] = agg[c].round(3)
+    for c in ("cost_per_q", "cli_cost_per_q"):
+        agg[c] = agg[c].round(4)
+    agg[["calls_per_q", "sec_per_q"]] = agg[["calls_per_q", "sec_per_q"]].round(1)
+    print(agg.to_string(index=False))
+
+    print("\nper source dataset (mean score):")
+    print(df.pivot_table(index="dataset", columns="tag", values="score", aggfunc="mean").round(2).to_string())
+    print("\nper task (mean score):")
+    print(df.pivot_table(index="task", columns="tag", values="score", aggfunc="mean").round(2).to_string())
+
+
+if __name__ == "__main__":
+    main(sys.argv[1:])