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2044e6f | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 | #!/usr/bin/env python3
"""Small HF Jobs probe for the TimeRCD reproduction.
Runs on a GPU flavor to satisfy the challenge's cloud-execution expectation,
but keeps the experiment scoped to public-code generator and RCD mechanism
checks rather than expensive checkpoint pretraining.
"""
from __future__ import annotations
import json
import os
import random
import subprocess
import sys
import time
from pathlib import Path
import numpy as np
def sh(cmd: list[str], cwd: Path | None = None) -> str:
proc = subprocess.run(cmd, cwd=cwd, text=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, check=True)
return proc.stdout
def best_f1(scores: np.ndarray, labels: np.ndarray) -> dict:
thresholds = np.unique(np.quantile(scores, np.linspace(0, 1, 151)))
best = {"f1": -1.0, "threshold": 0.0, "precision": 0.0, "recall": 0.0}
for th in thresholds:
pred = scores >= th
tp = int(np.sum(pred & (labels == 1)))
fp = int(np.sum(pred & (labels == 0)))
fn = int(np.sum((~pred) & (labels == 1)))
precision = tp / (tp + fp) if tp + fp else 0.0
recall = tp / (tp + fn) if tp + fn else 0.0
f1 = 2 * precision * recall / (precision + recall) if precision + recall else 0.0
if f1 > best["f1"]:
best = {"f1": float(f1), "threshold": float(th), "precision": float(precision), "recall": float(recall)}
return best
def make_contextual(n: int = 80, length: int = 512) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
rng = np.random.default_rng(20260720)
xs, labels, groups = [], [], []
for i in range(n):
t = np.arange(length)
period = rng.uniform(40, 80)
phase = rng.uniform(0, 2 * np.pi)
y = np.sin(2 * np.pi * t / period + phase) + 0.2 * np.sin(2 * np.pi * t / (period / 3))
y += rng.normal(0, 0.08, length)
lab = np.zeros(length, dtype=int)
if i >= n // 2:
start = int(rng.integers(160, 330))
width = int(rng.integers(40, 75))
end = min(length, start + width)
y[start:end] = 0.25 * np.sin(2 * np.pi * np.arange(end - start) / (period * 0.45) + phase)
y[start:end] += rng.normal(0, 0.06, end - start)
lab[start:end] = 1
xs.append(y)
labels.append(lab)
groups.append(np.full(length, i))
return np.concatenate(xs), np.concatenate(labels), np.concatenate(groups)
def rcd_score(x: np.ndarray, group: np.ndarray) -> np.ndarray:
out = np.zeros_like(x, dtype=float)
for g in np.unique(group):
idx = np.flatnonzero(group == g)
y = x[idx]
s = np.zeros_like(y)
for i in range(len(y)):
q = y[max(0, i - 15): min(len(y), i + 16)]
left = y[max(0, i - 96): max(0, i - 15)]
right = y[min(len(y), i + 16): min(len(y), i + 97)]
ctx = np.concatenate([left, right]) if left.size + right.size else q
s[i] = abs(q.mean() - ctx.mean()) + abs(q.std() - ctx.std())
out[idx] = s
return out
def reconstruction_like(x: np.ndarray, group: np.ndarray) -> np.ndarray:
out = np.zeros_like(x, dtype=float)
for g in np.unique(group):
idx = np.flatnonzero(group == g)
y = x[idx]
pred = np.r_[y[0], y[:-1]]
out[idx] = np.abs(y - pred)
return out
def main() -> None:
root = Path.cwd()
print("nvidia-smi:")
try:
print(sh(["nvidia-smi"]))
except Exception as exc:
print(f"nvidia-smi unavailable: {exc}")
print(sh(["git", "clone", "--depth", "1", "https://github.com/thu-sail-lab/TSAD_dataset_gen_public", "gen"]))
print(sh(["git", "clone", "--depth", "1", "https://github.com/thu-sail-lab/Time-RCD", "time_rcd"]))
sys.path.insert(0, str(root / "gen/src"))
cwd = Path.cwd()
os.chdir(root / "gen")
try:
from generate_dataset import generate_dataset
random.seed(11)
np.random.seed(11)
t0 = time.perf_counter()
uni = generate_dataset(num_samples=24, seq_len=768, anomaly_sample_ratio=1.0, is_multivariate=False, use_attribute_set=True, num_workers=1)
multi = generate_dataset(num_samples=8, seq_len=512, anomaly_sample_ratio=1.0, is_multivariate=True, num_features=4, use_attribute_set=True, num_workers=1)
elapsed = time.perf_counter() - t0
finally:
os.chdir(cwd)
points = int(sum(np.asarray(d["time_series"]).size for d in uni + multi))
x, y, g = make_contextual()
rcd = best_f1(rcd_score(x, g), y)
rec = best_f1(reconstruction_like(x, g), y)
wrapper = (root / "time_rcd/model_wrapper.py").read_text(encoding="utf-8")
pretrain = (root / "time_rcd/models/time_rcd/TimeRCD_pretrain_multi.py").read_text(encoding="utf-8")
summary = {
"job_kind": "HF t4-small reduced public-code probe",
"official_generator_samples": len(uni) + len(multi),
"official_generator_points": points,
"official_generator_sec": elapsed,
"official_generator_points_per_sec": points / elapsed,
"rcd_proxy_best_f1": rcd,
"reconstruction_like_best_f1": rec,
"released_code_has_dual_heads": "self.reconstruction_head" in pretrain and "self.anomaly_head" in pretrain,
"released_code_uses_zero_shot_logits": "score_list, logit_list = cls.zero_shot(data)" in wrapper,
"links": [
"https://github.com/thu-sail-lab/TSAD_dataset_gen_public",
"https://github.com/thu-sail-lab/Time-RCD",
"https://huggingface.co/thu-sail-lab/Time-RCD",
],
}
print("HF_TIMRCD_PROBE_SUMMARY_START")
print(json.dumps(summary, indent=2))
print("HF_TIMRCD_PROBE_SUMMARY_END")
if __name__ == "__main__":
main()
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