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# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import math
from dataclasses import dataclass
import numpy as np
P01_TWO_SIDED = 2.576
def standard_error_from_tailstd(
tailstd: float,
*,
n_systems: int,
batch_size: int,
) -> float:
if tailstd is None or not math.isfinite(float(tailstd)):
return float("nan")
denominator = max(1, int(n_systems) * int(batch_size))
return float(tailstd) / math.sqrt(denominator)
def welch_band_walkermean(
tailstd_a: float,
tailstd_b: float,
*,
n_systems_a: int,
batch_size_a: int,
n_systems_b: int,
batch_size_b: int,
z: float = P01_TWO_SIDED,
) -> float:
se_a = standard_error_from_tailstd(
tailstd_a,
n_systems=n_systems_a,
batch_size=batch_size_a,
)
se_b = standard_error_from_tailstd(
tailstd_b,
n_systems=n_systems_b,
batch_size=batch_size_b,
)
if not (math.isfinite(se_a) and math.isfinite(se_b)):
return float("nan")
return float(z) * math.sqrt(se_a * se_a + se_b * se_b)
def select_winner_per_physical(
R,
tailstd,
beam_logp,
batch_per_candidate,
*,
z: float = P01_TWO_SIDED,
ucb_z: float = 2.0,
):
R = np.asarray(R, dtype=float)
tailstd = np.asarray(tailstd, dtype=float)
beam_logp = np.asarray(beam_logp, dtype=float)
P, K = R.shape
Bp = int(batch_per_candidate)
winner_idx = np.zeros(P, dtype=np.int64)
tie_mask = np.zeros((P, K), dtype=bool)
bands = np.full((P, K), np.nan, dtype=float)
se = np.array(
[
[
standard_error_from_tailstd(
tailstd[p, k],
n_systems=1,
batch_size=Bp,
)
for k in range(K)
]
for p in range(P)
],
dtype=float,
)
reason: list[str] = []
for p in range(P):
Rp, sp, lp = R[p], tailstd[p], beam_logp[p]
finite = np.isfinite(Rp)
if not finite.any():
w = int(np.argmax(lp))
winner_idx[p] = w
tie_mask[p, w] = True
reason.append("energy_unavailable")
continue
istar = int(np.argmin(np.where(finite, Rp, np.inf)))
for k in range(K):
band = welch_band_walkermean(
sp[k],
sp[istar],
n_systems_a=1,
batch_size_a=Bp,
n_systems_b=1,
batch_size_b=Bp,
z=z,
)
bands[p, k] = band
if k == istar:
tie_mask[p, k] = True
elif finite[k] and math.isfinite(band) and abs(Rp[k] - Rp[istar]) < band:
tie_mask[p, k] = True
tie_idx = np.where(tie_mask[p])[0]
se_tie = se[p, tie_idx]
ucb = Rp[tie_idx] + ucb_z * np.where(np.isfinite(se_tie), se_tie, np.inf)
w = int(tie_idx[int(np.argmin(ucb))]) if np.any(np.isfinite(ucb)) else istar
winner_idx[p] = w
reason.append("argmin_energy" if w == istar else "tie_broken_by_ucb")
return winner_idx, tie_mask, reason, bands, se
@dataclass(frozen=True, slots=True)
class ChannelMetrics:
mean: float
standard_error: float
walker_tail_std: float
local_energy_std: float
lag1_autocorrelation: float | None
def as_dict(self) -> dict[str, float | None]:
return {
"mean": self.mean,
"standard_error": self.standard_error,
"walker_tail_std": self.walker_tail_std,
"local_energy_std": self.local_energy_std,
"lag1_autocorrelation": self.lag1_autocorrelation,
}
class EnergyWindow:
def __init__(self, steps: int, systems: int, batch_size: int):
self.steps = int(steps)
self.systems = int(systems)
self.batch_size = int(batch_size)
shape = (self.steps, self.systems, self.batch_size)
self.values = {
"total": np.empty(shape, dtype=np.float32),
"exchange": np.empty(shape, dtype=np.float32),
"field": np.empty(shape, dtype=np.float32),
}
self.sums = {
"total": np.zeros((self.systems, self.batch_size), dtype=np.float32),
"exchange": np.zeros((self.systems, self.batch_size), dtype=np.float32),
"field": np.zeros((self.systems, self.batch_size), dtype=np.float32),
}
self.count = 0
def push(self, total, exchange, field) -> None:
if self.count >= self.steps:
raise IndexError("energy window is full")
for name, value in (
("total", total),
("exchange", exchange),
("field", field),
):
array = np.asarray(value).real.astype(np.float32, copy=False)
expected = (self.systems, self.batch_size)
if array.shape != expected:
raise ValueError(
f"{name} energy must have shape {expected}, got {array.shape}"
)
self.values[name][self.count] = array
self.sums[name] += array
self.count += 1
def tail_means(self, channel: str) -> np.ndarray:
if self.count < 1:
raise RuntimeError("energy window is empty")
return (self.sums[channel] / float(self.count)).astype(np.float32, copy=False)
def tail_mean(self, channel: str, system: int) -> float:
return float(np.mean(self.tail_means(channel)[system]))
def tail_std(self, channel: str, system: int) -> float:
per_walker = np.asarray(self.tail_means(channel)[system]).ravel()
if per_walker.size < 2:
return float("nan")
return float(np.std(per_walker, ddof=1))
def metrics(self, channel: str) -> ChannelMetrics:
if self.systems != 1:
raise ValueError("bare eval metrics require one physical system")
samples = self.values[channel][: self.count, 0]
per_walker = self.tail_means(channel)[0]
mean = float(np.mean(per_walker))
tailstd = (
float(np.std(per_walker, ddof=1)) if per_walker.size >= 2 else float("nan")
)
se = standard_error_from_tailstd(
tailstd,
n_systems=1,
batch_size=self.batch_size,
)
step_std = np.std(samples, axis=1)
gap_sq = float(np.nanmean(step_std**2))
local_std = (
math.sqrt(gap_sq)
if math.isfinite(gap_sq) and gap_sq >= 0.0
else float("nan")
)
step_means = np.mean(samples, axis=1)
finite = step_means[np.isfinite(step_means)]
autocorrelation = None
if len(finite) >= 20:
centered = finite - np.mean(finite)
denominator = float(np.sum(centered * centered))
if denominator > 0.0:
autocorrelation = float(
np.sum(centered[:-1] * centered[1:]) / denominator
)
return ChannelMetrics(
mean=mean,
standard_error=se,
walker_tail_std=tailstd,
local_energy_std=local_std,
lag1_autocorrelation=autocorrelation,
)
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