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"""Paired comparison of PTRM rollout caches.
This focuses on per-problem correct rollout counts, not only best-Q accuracy.
Use it when two caches were generated with the same sample seed so `idx` aligns.
"""
from __future__ import annotations
import argparse
import csv
from pathlib import Path
import numpy as np
def _load(path: Path) -> dict[str, np.ndarray]:
data = np.load(path)
return {name: data[name] for name in data.files}
def _take(values: np.ndarray, idx: np.ndarray) -> np.ndarray:
return values[np.arange(values.shape[0]), idx]
def _summary(prefix: str, exact: np.ndarray, q_halt: np.ndarray, det_exact: np.ndarray) -> dict[str, float]:
correct_count = exact.sum(axis=1).astype(np.float32)
q_sel = _take(exact, q_halt.argmax(axis=1)).astype(bool)
oracle = exact.any(axis=1)
out = {
f"{prefix}/det_exact": float(det_exact.mean()),
f"{prefix}/q_max_exact": float(q_sel.mean()),
f"{prefix}/oracle_pass": float(oracle.mean()),
f"{prefix}/mean_rollout_exact": float(exact.mean()),
f"{prefix}/correct_count_mean": float(correct_count.mean()),
f"{prefix}/correct_count_median": float(np.median(correct_count)),
f"{prefix}/correct_count_q10": float(np.quantile(correct_count, 0.10)),
f"{prefix}/correct_count_q25": float(np.quantile(correct_count, 0.25)),
f"{prefix}/correct_count_q75": float(np.quantile(correct_count, 0.75)),
f"{prefix}/correct_count_q90": float(np.quantile(correct_count, 0.90)),
f"{prefix}/zero_correct_frac": float((correct_count == 0).mean()),
f"{prefix}/all_correct_frac": float((correct_count == exact.shape[1]).mean()),
}
for threshold in (1, 5, 10, 25, 50, 75, 90):
if threshold <= exact.shape[1]:
out[f"{prefix}/correct_count_ge_{threshold}_frac"] = float((correct_count >= threshold).mean())
for name, mask in {
"det_success": det_exact.astype(bool),
"det_fail": ~det_exact.astype(bool),
}.items():
if mask.any():
out[f"{prefix}/correct_count_{name}_mean"] = float(correct_count[mask].mean())
out[f"{prefix}/oracle_{name}_frac"] = float(oracle[mask].mean())
out[f"{prefix}/q_max_{name}_frac"] = float(q_sel[mask].mean())
return out
def _prefix_curves(prefix: str, exact: np.ndarray, q_halt: np.ndarray) -> tuple[dict[str, float], list[dict[str, float]]]:
rows: list[dict[str, float]] = []
out: dict[str, float] = {}
max_k = exact.shape[1]
for k in range(1, max_k + 1):
exact_k = exact[:, :k]
q_k = q_halt[:, :k]
oracle = exact_k.any(axis=1)
q_sel = _take(exact_k, q_k.argmax(axis=1)).astype(bool)
count = exact_k.sum(axis=1).astype(np.float32)
row = {
"K": float(k),
f"{prefix}/oracle_pass": float(oracle.mean()),
f"{prefix}/q_max_exact": float(q_sel.mean()),
f"{prefix}/correct_count_mean": float(count.mean()),
f"{prefix}/zero_correct_frac": float((count == 0).mean()),
f"{prefix}/correct_count_ge_5_frac": float((count >= min(5, k)).mean()),
f"{prefix}/correct_count_ge_half_frac": float((count >= (k / 2.0)).mean()),
}
rows.append(row)
for target in (0.90, 0.95, 0.975, 0.99):
for metric in ("oracle_pass", "q_max_exact"):
needed = next(
(int(row["K"]) for row in rows if row[f"{prefix}/{metric}"] >= target),
None,
)
out[f"{prefix}/K_for_{metric}_{target:g}"] = float("nan") if needed is None else float(needed)
return out, rows
def compare(base_path: Path, test_path: Path, base_label: str, test_label: str) -> tuple[dict[str, float | str], list[dict[str, float]]]:
base = _load(base_path)
test = _load(test_path)
if "idx" in base and "idx" in test and not np.array_equal(base["idx"], test["idx"]):
raise ValueError("Caches are not paired: idx arrays differ.")
base_exact = base["exact"].astype(bool)
test_exact = test["exact"].astype(bool)
base_q = base["q_halt"].astype(np.float32)
test_q = test["q_halt"].astype(np.float32)
base_det = base["det_exact"].astype(bool)
test_det = test["det_exact"].astype(bool)
base_count = base_exact.sum(axis=1).astype(np.float32)
test_count = test_exact.sum(axis=1).astype(np.float32)
delta = test_count - base_count
base_oracle = base_exact.any(axis=1)
test_oracle = test_exact.any(axis=1)
base_q_sel = _take(base_exact, base_q.argmax(axis=1)).astype(bool)
test_q_sel = _take(test_exact, test_q.argmax(axis=1)).astype(bool)
out: dict[str, float | str] = {
"base_file": str(base_path),
"test_file": str(test_path),
"n_samples": float(base_exact.shape[0]),
"rollouts": float(base_exact.shape[1]),
}
out.update(_summary(base_label, base_exact, base_q, base_det))
out.update(_summary(test_label, test_exact, test_q, test_det))
base_curve_summary, base_curve_rows = _prefix_curves(base_label, base_exact, base_q)
test_curve_summary, test_curve_rows = _prefix_curves(test_label, test_exact, test_q)
out.update(base_curve_summary)
out.update(test_curve_summary)
curve_rows: list[dict[str, float]] = []
for base_row, test_row in zip(base_curve_rows, test_curve_rows, strict=True):
row = {**base_row, **{k: v for k, v in test_row.items() if k != "K"}}
k = int(row["K"])
base_prefix = f"{base_label}/"
test_prefix = f"{test_label}/"
row["delta/oracle_pass"] = row[f"{test_prefix}oracle_pass"] - row[f"{base_prefix}oracle_pass"]
row["delta/q_max_exact"] = row[f"{test_prefix}q_max_exact"] - row[f"{base_prefix}q_max_exact"]
row["delta/correct_count_mean"] = row[f"{test_prefix}correct_count_mean"] - row[f"{base_prefix}correct_count_mean"]
curve_rows.append(row)
out.update(
{
"delta_correct_count_mean": float(delta.mean()),
"delta_correct_count_median": float(np.median(delta)),
"delta_correct_count_q10": float(np.quantile(delta, 0.10)),
"delta_correct_count_q25": float(np.quantile(delta, 0.25)),
"delta_correct_count_q75": float(np.quantile(delta, 0.75)),
"delta_correct_count_q90": float(np.quantile(delta, 0.90)),
"test_more_correct_frac": float((delta > 0).mean()),
"test_equal_correct_frac": float((delta == 0).mean()),
"test_fewer_correct_frac": float((delta < 0).mean()),
"base_zero_test_nonzero_frac": float(((base_count == 0) & (test_count > 0)).mean()),
"base_nonzero_test_zero_frac": float(((base_count > 0) & (test_count == 0)).mean()),
"both_oracle_success_test_more_frac": float((base_oracle & test_oracle & (delta > 0)).mean()),
"oracle_test_only_frac": float((~base_oracle & test_oracle).mean()),
"oracle_base_only_frac": float((base_oracle & ~test_oracle).mean()),
"oracle_both_success_frac": float((base_oracle & test_oracle).mean()),
"oracle_both_fail_frac": float((~base_oracle & ~test_oracle).mean()),
"q_max_test_only_frac": float((~base_q_sel & test_q_sel).mean()),
"q_max_base_only_frac": float((base_q_sel & ~test_q_sel).mean()),
"q_max_both_success_frac": float((base_q_sel & test_q_sel).mean()),
"q_max_both_fail_frac": float((~base_q_sel & ~test_q_sel).mean()),
}
)
hist_rows: list[dict[str, float]] = []
max_count = int(max(base_count.max(), test_count.max()))
for count in range(max_count + 1):
hist_rows.append(
{
"correct_count": float(count),
f"{base_label}_frac": float((base_count == count).mean()),
f"{test_label}_frac": float((test_count == count).mean()),
}
)
return out, hist_rows, curve_rows
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("--base", required=True)
parser.add_argument("--test", required=True)
parser.add_argument("--base-label", default="base")
parser.add_argument("--test-label", default="test")
parser.add_argument("--out-prefix", default=None)
args = parser.parse_args()
summary, hist_rows, curve_rows = compare(Path(args.base), Path(args.test), args.base_label, args.test_label)
for key in sorted(summary):
print(f"{key}: {summary[key]}")
if args.out_prefix:
prefix = Path(args.out_prefix)
prefix.parent.mkdir(parents=True, exist_ok=True)
with (prefix.with_suffix(".summary.csv")).open("w", newline="") as f:
writer = csv.DictWriter(f, fieldnames=sorted(summary))
writer.writeheader()
writer.writerow(summary)
with (prefix.with_suffix(".hist.csv")).open("w", newline="") as f:
writer = csv.DictWriter(f, fieldnames=list(hist_rows[0]))
writer.writeheader()
writer.writerows(hist_rows)
with (prefix.with_suffix(".kcurve.csv")).open("w", newline="") as f:
writer = csv.DictWriter(f, fieldnames=list(curve_rows[0]))
writer.writeheader()
writer.writerows(curve_rows)
print(
f"saved {prefix.with_suffix('.summary.csv')}, "
f"{prefix.with_suffix('.hist.csv')}, and {prefix.with_suffix('.kcurve.csv')}"
)
if __name__ == "__main__":
main()
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