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60da8fb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 | """Tolerance-matched runtime comparison: BBQ (EM) vs Crowd-BT (online gradient).
Both estimators are stopped by the SAME criterion -- the largest change in the
log-scale item score over one full sweep of the data -- so the seconds-vs-
minutes comparison of Figure 3 is measured rather than asserted.
"""
import json, math, time, sys
import numpy as np
import pandas as pd
from bbq_vs_crowdbt import load_clic, load_humaine, encode, ALPHA0, BETA0, MU0, SIGMA_SQ0, KAPPA
TOLS = [1e-2, 1e-3, 1e-4, 1e-5, 1e-6]
def bbq_timed(i_idx, j_idx, r_idx, K, R, a=5.0, b=0.1, alpha=10.0, beta=2.0, max_iter=4000):
lam = np.ones(K); q = np.full(R, 0.5)
w = np.ones(i_idx.size)
n_r = np.bincount(r_idx, minlength=R).astype(float)
hit = {}; t0 = time.perf_counter(); mind = np.inf; ll_prev = None
for it in range(max_iter):
y = lam[i_idx] / (lam[i_idx] + lam[j_idx])
qc = q[r_idx]
g = qc * y / (qc * y + (1.0 - qc) * 0.5)
q_new = np.clip((np.bincount(r_idx, weights=g, minlength=R) + (alpha - 1.0))
/ (n_r + alpha + beta - 2.0), 1e-12, 1 - 1e-12)
wins = np.bincount(i_idx, weights=g, minlength=K) + (a - 1.0)
den = np.zeros(K); s = lam[i_idx] + lam[j_idx]
np.add.at(den, i_idx, g / s); np.add.at(den, j_idx, g / s)
lam_new = wins / (den + b)
shift = float(np.max(np.abs(np.log(lam_new) - np.log(lam))))
yi = lam_new[i_idx] / (lam_new[i_idx] + lam_new[j_idx]); qq = q_new[r_idx]
ll = float(np.sum(np.log(qq * yi + (1 - qq) * 0.5))
+ np.sum((a - 1) * np.log(lam_new) - b * lam_new)
+ np.sum((alpha - 1) * np.log(q_new) + (beta - 1) * np.log1p(-q_new)))
if ll_prev is not None:
mind = min(mind, ll - ll_prev)
ll_prev = ll
lam, q = lam_new, q_new
for tol in TOLS:
if tol not in hit and shift < tol:
hit[tol] = (it + 1, time.perf_counter() - t0)
if len(hit) == len(TOLS):
break
return lam, q, hit, it + 1, time.perf_counter() - t0, mind
def crowdbt_timed(i_idx, j_idx, r_idx, K, R, max_epochs=600, seed=0):
mu = np.full(K, MU0); ss = np.full(K, SIGMA_SQ0)
al = np.full(R, ALPHA0); be = np.full(R, BETA0)
rng = np.random.default_rng(seed)
order = np.arange(i_idx.size)
hit = {}; t0 = time.perf_counter(); traj = []
ii = i_idx.tolist(); jj = j_idx.tolist(); rr = r_idx.tolist()
for ep in range(max_epochs):
rng.shuffle(order)
prev = mu.copy()
for t in order:
w = ii[t]; l = jj[t]; k = rr[t]
mw = mu[w]; ml = mu[l]; sw = ss[w]; sl = ss[l]
a = al[k]; b = be[k]
m = mw - ml
c1 = 1.0 if m > 30 else (0.0 if m < -30 else 1.0 / (1.0 + math.exp(-m)))
c2 = 1.0 - c1
ab = a + b
dnm = a * c1 + b * c2
if dnm <= 0:
continue
d = (a - b) * c1 * c2 / dnm
mu[w] = mw + sw * d
mu[l] = ml - sl * d
h = (a - b) * c1 * c2 * (c2 - c1) / dnm - d * d
fac = h - d * d
ss[w] = sw * max(1.0 + sw * fac, KAPPA)
ss[l] = sl * max(1.0 + sl * fac, KAPPA)
f = a * c1 / dnm
e1 = (f * (a + 1.0) + (1.0 - f) * a) / (ab + 1.0)
e2 = (f * (a + 1.0) * (a + 2.0) + (1.0 - f) * a * (a + 1.0)) / ((ab + 1.0) * (ab + 2.0))
v = e2 - e1 * e1
if v > 1e-12 and e1 > e2 and 0.0 < e1 < 1.0:
an = e1 * (e1 - e2) / v; bn = (1.0 - e1) * (e1 - e2) / v
if an > 0 and bn > 0 and math.isfinite(an) and math.isfinite(bn):
al[k] = min(an, 1e6); be[k] = min(bn, 1e6)
shift = float(np.max(np.abs(mu - prev)))
traj.append(shift)
for tol in TOLS:
if tol not in hit and shift < tol:
hit[tol] = (ep + 1, time.perf_counter() - t0)
if len(hit) == len(TOLS):
break
return mu, al / (al + be), hit, ep + 1, time.perf_counter() - t0, traj
def run(name, df, max_epochs):
i, j, r, K, R = encode(df)
lam, q, bh, bi, bt, mind = bbq_timed(i, j, r, K, R)
mu, eta, ch, ce, ct, traj = crowdbt_timed(i, j, r, K, R, max_epochs=max_epochs)
from scipy.stats import kendalltau
out = dict(dataset=name, comparisons=int(i.size), items=int(K), raters=int(R),
bbq_total_iters=bi, bbq_total_secs=round(bt, 4),
bbq_min_logpost_delta=mind,
bbq_hit={str(k): [v[0], round(v[1], 4)] for k, v in bh.items()},
cbt_total_epochs=ce, cbt_total_secs=round(ct, 3),
cbt_hit={str(k): [v[0], round(v[1], 3)] for k, v in ch.items()},
cbt_final_shift=round(traj[-1], 8),
kendall_bbq_vs_cbt=round(float(kendalltau(np.log(lam), mu).statistic), 4))
print(json.dumps(out), flush=True)
return out
if __name__ == "__main__":
which = sys.argv[1]
out = []
if which == "small":
for s in ["screened", "unscreened"]:
out.append(run("IHQ-" + s, load_clic(s), 4000))
out.append(run("IHQ-all", pd.concat([load_clic("screened"), load_clic("unscreened")],
ignore_index=True), 4000))
elif which == "mtbench":
from datasets import load_dataset
ds = load_dataset("lmsys/mt_bench_human_judgments")
rows = []
for split in ds:
for ex in ds[split]:
w = ex.get("winner")
if w == "model_a":
rows.append(dict(rater=str(ex.get("judge")), winner=ex["model_a"], loser=ex["model_b"]))
elif w == "model_b":
rows.append(dict(rater=str(ex.get("judge")), winner=ex["model_b"], loser=ex["model_a"]))
out.append(run("MT-Bench", pd.DataFrame(rows), 4000))
else:
h = load_humaine()
sub = h.sample(n=105220, random_state=20260802).reset_index(drop=True)
out.append(run("HUMAINE-105220", sub, 600))
with open("timing2_%s.json" % which, "w") as f:
json.dump(out, f, indent=1)
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