analyze.py

"""Turn raw job JSON into the numbers behind each claim.

Usage: python analyze.py <claim> <results.json> [more.json ...]
"""

from __future__ import annotations

import glob
import json
import sys

import numpy as np


def load(paths):
    out = []
    for p in paths:
        for f in sorted(glob.glob(p)):
            with open(f) as fh:
                out.append(json.load(fh))
    return out


# --------------------------------------------------------------------------
# Claim 1 / Claim 5: from the ablation records
# --------------------------------------------------------------------------


def claim1(paths):
    """Proposition 1: the standard (EIG) utility choice is suboptimal w.r.t. the
    Lagrangian utility that accounts for the drifter's future trajectory.

    The paper's formal proof (App. D) is a one-line argmax argument giving only
    the weak inequality LU(x^S) <= LU(x*). Here we measure whether the gap is
    real and how big it is, using the ground-truth field to define LU = B(.;true).
    """
    recs = [r for d in load(paths) for r in d["records"]]
    rows = []
    for r in recs:
        u_true = np.array(r["u_true"])
        u_eig = np.array(r["u_eig"])
        u_ball = np.array(r["u"]).mean(0)
        i_eig, i_ball, i_star = u_eig.argmax(), u_ball.argmax(), u_true.argmax()
        rows.append({
            "t": r["t"],
            "LU_star": u_true[i_star],
            "LU_eig": u_true[i_eig],
            "LU_ballast": u_true[i_ball],
            "LU_mean": u_true.mean(),  # uniform policy
            "strict": bool(u_true[i_eig] < u_true[i_star] - 1e-9),
            "eig_is_argmax": bool(i_eig == i_star),
        })
    out = {}
    for t in sorted(set(r["t"] for r in rows)):
        sub = [r for r in rows if r["t"] == t]
        gap_eig = np.array([(r["LU_star"] - r["LU_eig"]) / abs(r["LU_star"]) * 100 for r in sub])
        gap_bal = np.array([(r["LU_star"] - r["LU_ballast"]) / abs(r["LU_star"]) * 100 for r in sub])
        gap_uni = np.array([(r["LU_star"] - r["LU_mean"]) / abs(r["LU_star"]) * 100 for r in sub])
        out[t] = {
            "n": len(sub),
            "pct_strictly_suboptimal": 100 * np.mean([r["strict"] for r in sub]),
            "gap_eig_pct": [gap_eig.mean(), 2 * gap_eig.std() / np.sqrt(len(sub))],
            "gap_ballast_pct": [gap_bal.mean(), 2 * gap_bal.std() / np.sqrt(len(sub))],
            "gap_unif_pct": [gap_uni.mean(), 2 * gap_uni.std() / np.sqrt(len(sub))],
        }
    return out


def claim5(paths):
    """Sec. 5.1 / G.1: percentage utility gap vs J, and the J at which it drops
    below 1%.

        Gap_MC(J)   = B(s*; inf) - B(s*_J; inf),  approximated with J=200
        Gap_Full(J) = B(s*_true; true) - B(s*_J; true)
    """
    recs = [r for d in load(paths) for r in d["records"]]
    ts = sorted(set(r["t"] for r in recs))
    out = {}
    for t in ts:
        sub = [r for r in recs if r["t"] == t]
        Jmax = np.array(sub[0]["u"]).shape[0]
        Js = np.arange(1, Jmax + 1)
        mc = np.zeros((len(sub), Jmax))
        full = np.zeros((len(sub), Jmax))
        eig_mc, eig_full, uni_mc, uni_full = [], [], [], []
        for i, r in enumerate(sub):
            u = np.array(r["u"])  # (Jmax, N)
            u_true = np.array(r["u_true"])
            u_eig = np.array(r["u_eig"])
            B_inf = u.mean(0)  # B(.; inf) approximated by J=200
            s_star = B_inf.argmax()
            s_true = u_true.argmax()
            run = np.cumsum(u, axis=0) / Js[:, None]  # B(.; J) for each J
            sJ = run.argmax(axis=1)  # s*_J
            mc[i] = (B_inf[s_star] - B_inf[sJ]) / abs(B_inf[s_star]) * 100
            full[i] = (u_true[s_true] - u_true[sJ]) / abs(u_true[s_true]) * 100
            ie = u_eig.argmax()
            eig_mc.append((B_inf[s_star] - B_inf[ie]) / abs(B_inf[s_star]) * 100)
            eig_full.append((u_true[s_true] - u_true[ie]) / abs(u_true[s_true]) * 100)
            uni_mc.append((B_inf[s_star] - B_inf.mean()) / abs(B_inf[s_star]) * 100)
            uni_full.append((u_true[s_true] - u_true.mean()) / abs(u_true[s_true]) * 100)

        def band(a):
            return a.mean(0), 2 * a.std(0) / np.sqrt(a.shape[0])

        m_mc, s_mc = band(mc)
        m_fu, s_fu = band(full)
        below = np.where(m_mc < 1.0)[0]
        out[t] = {
            "n_reps": len(sub),
            "J": Js.tolist(),
            "gap_mc_mean": m_mc.tolist(), "gap_mc_se2": s_mc.tolist(),
            "gap_full_mean": m_fu.tolist(), "gap_full_se2": s_fu.tolist(),
            "J_at_1pct_mc": int(Js[below[0]]) if len(below) else None,
            "eig_gap_mc": float(np.mean(eig_mc)), "eig_gap_full": float(np.mean(eig_full)),
            "unif_gap_mc": float(np.mean(uni_mc)), "unif_gap_full": float(np.mean(uni_full)),
            "gap_at_J20_mc": float(m_mc[19]), "gap_at_J20_full": float(m_fu[19]),
        }
    return out


# --------------------------------------------------------------------------
# Claims 3 / 4: policy comparison
# --------------------------------------------------------------------------

POLICY_ORDER = ["unif", "sobol", "dist_sep", "eig", "ballast_opt", "ballast_true"]


def claim34(paths):
    from ballast.experiment import iso_performance

    res = [r for d in load(paths) for r in d["results"]]
    seeds = sorted(set(r["seed"] for r in res))
    pols = [p for p in POLICY_ORDER if any(r["policy"] == p for r in res)]
    by = {(r["seed"], r["policy"]): np.array(r["errors"]) for r in res}
    n_dep = len(next(iter(by.values())))

    # runs where every policy completed
    good = [s for s in seeds if all((s, p) in by for p in pols)]
    E = {p: np.stack([by[(s, p)] for s in good]) for p in pols}  # (n_runs, n_dep)

    # --- average policy rank per iteration (1 = best)
    stack = np.stack([E[p] for p in pols])  # (n_pol, n_runs, n_dep)
    order = stack.argsort(axis=0).argsort(axis=0) + 1
    rank_mean = order.mean(axis=1)  # (n_pol, n_dep)
    rank_se2 = 2 * order.std(axis=1) / np.sqrt(len(good))

    # --- iso-performance vs UNIF
    iso = {}
    for p in pols:
        v = np.stack([iso_performance(E[p][i], E["unif"][i]) for i in range(len(good))])
        iso[p] = {
            "mean": np.nanmean(v, axis=0).tolist(),
            "se2": (2 * np.nanstd(v, axis=0) / np.sqrt(len(good))).tolist(),
            "final": float(np.nanmean(v[:, -1])),
            "final_se2": float(2 * np.nanstd(v[:, -1]) / np.sqrt(len(good))),
        }
    n_policy_chosen = n_dep - 1  # the first drifter is placed uniformly at random

    # The paper defines iso-performance as "averaged over each iteration's
    # results" (Sec. 5.2) and reports the saving as a single number ("save about
    # 3 drifters ~16%"). We therefore headline the mean over deployment
    # iterations, which matches the paper's Claim-3 number almost exactly (3.4 vs
    # 3). The final-iteration value ("drifters needed to match uniform's *final*
    # accuracy") is a different, larger statistic and is kept as a secondary read.
    iso_avg = {p: float(np.nanmean(iso[p]["mean"])) for p in pols}
    iso_avg_se2 = {
        p: float(2 * np.nanstd([np.nanmean(
            [iso_performance(E[p][i], E["unif"][i])]) for i in range(len(good))])
            / np.sqrt(len(good)))
        for p in pols
    }
    # per-run averaged-over-iterations, for a correct standard error
    iso_avg_runs = {
        p: np.array([np.nanmean(iso_performance(E[p][i], E["unif"][i]))
                     for i in range(len(good))])
        for p in pols
    }
    iso_avg = {p: float(np.nanmean(iso_avg_runs[p])) for p in pols}
    iso_avg_se2 = {p: float(2 * np.nanstd(iso_avg_runs[p]) / np.sqrt(len(good)))
                   for p in pols}
    return {
        "n_runs": len(good),
        "policies": pols,
        "n_deploy": n_dep,
        "rank_mean": rank_mean.tolist(),
        "rank_se2": rank_se2.tolist(),
        "err_mean": {p: E[p].mean(0).tolist() for p in pols},
        "err_se2": {p: (2 * E[p].std(0) / np.sqrt(len(good))).tolist() for p in pols},
        "iso": iso,
        "iso_avg": iso_avg,  # paper's metric: averaged over iterations
        "iso_avg_se2": iso_avg_se2,
        "savings_pct_avg": {p: 100 * iso_avg[p] / n_policy_chosen for p in pols},
        "savings_pct_final": {p: 100 * iso[p]["final"] / n_policy_chosen for p in pols},
        "n_obs_mean": {
            p: float(np.mean([r["n_obs"] for r in res if r["policy"] == p])) for p in pols
        },
    }


if __name__ == "__main__":
    which, paths = sys.argv[1], sys.argv[2:]
    fn = {"claim1": claim1, "claim5": claim5, "claim34": claim34}[which]
    print(json.dumps(fn(paths), indent=2, default=float))