File size: 4,894 Bytes
5cc5437 | 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 | #!/usr/bin/env python3
"""Small released-checkpoint probe on generated contextual anomalies.
This is not the paper's full contextual benchmark. It is a sanity check that the
public univariate checkpoint can run zero-shot on contextual anomaly series and
emit anomaly-head scores end to end in this reproduction environment.
"""
from __future__ import annotations
import json
import os
import sys
from pathlib import Path
import numpy as np
ROOT = Path(__file__).resolve().parents[1]
TIME_RCD = ROOT / "official_Time-RCD"
OUT = ROOT / "outputs" / "deep_repro"
def best_f1(scores: np.ndarray, labels: np.ndarray) -> dict[str, float]:
scores = np.asarray(scores, dtype=float).reshape(-1)
labels = np.asarray(labels, dtype=int).reshape(-1)
n = min(len(scores), len(labels))
scores, labels = scores[:n], labels[:n]
thresholds = np.unique(np.quantile(scores, np.linspace(0, 1, 201)))
best = {"f1": 0.0, "precision": 0.0, "recall": 0.0, "threshold": float(thresholds[0])}
for threshold in thresholds:
pred = scores >= threshold
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), "precision": float(precision), "recall": float(recall), "threshold": float(threshold)}
return best
def make_contextual(n_series: int = 20, length: int = 512) -> tuple[np.ndarray, np.ndarray]:
rng = np.random.default_rng(12052)
series = []
labels = []
for i in range(n_series):
t = np.arange(length)
period = rng.uniform(42, 90)
phase = rng.uniform(0, 2 * np.pi)
y = np.sin(2 * np.pi * t / period + phase)
y += 0.24 * np.sin(2 * np.pi * t / (period / 3.0) + phase / 3)
y += rng.normal(0, 0.06, length)
lab = np.zeros(length, dtype=int)
if i >= n_series // 2:
start = int(rng.integers(150, 330))
width = int(rng.integers(45, 85))
end = min(length, start + width)
local = np.arange(end - start)
# Contextual: in-range and smooth, but locally violates the context
# relationship between dominant and harmonic components.
y[start:end] = 0.38 * np.sin(2 * np.pi * local / (period * 0.52) + phase)
y[start:end] += rng.normal(0, 0.05, end - start)
lab[start:end] = 1
series.append(y)
labels.append(lab)
return np.concatenate(series).reshape(-1, 1).astype("float32"), np.concatenate(labels)
def reconstruction_like(data: np.ndarray) -> np.ndarray:
flat = data.reshape(-1)
return np.abs(flat - np.r_[flat[0], flat[:-1]])
def main() -> None:
OUT.mkdir(parents=True, exist_ok=True)
sys.path.insert(0, str(TIME_RCD))
old_cwd = Path.cwd()
os.chdir(TIME_RCD)
try:
from models.TimeRCD import TimeRCDPretrainTester
from models.time_rcd.time_rcd_config import default_config
data, labels = make_contextual()
config = default_config
config.ts_config.patch_size = 16
config.ts_config.num_features = 1
config.win_size = 512
config.batch_size = 4
tester = TimeRCDPretrainTester("best_model/pretrain_checkpoint_best_uni.pth", config)
score_batches, logit_batches = tester.zero_shot(data)
finally:
os.chdir(old_cwd)
scores = np.concatenate([np.asarray(x).reshape(-1) for x in score_batches])
logits = np.concatenate([np.asarray(x).reshape(-1) for x in logit_batches])
rec = reconstruction_like(data)
result = {
"probe_scope": "20 generated contextual series, length 512; released univariate checkpoint; CPU/GPU auto device",
"data_points": int(data.shape[0]),
"anomaly_points": int(labels.sum()),
"checkpoint": "https://huggingface.co/thu-sail-lab/Time-RCD",
"code": "https://github.com/thu-sail-lab/Time-RCD",
"anomaly_probability_best_f1": best_f1(scores, labels),
"anomaly_logit_best_f1": best_f1(logits, labels),
"reconstruction_like_best_f1": best_f1(rec, labels),
"score_summary": {
"prob_min": float(np.min(scores)),
"prob_max": float(np.max(scores)),
"prob_mean": float(np.mean(scores)),
"logit_min": float(np.min(logits)),
"logit_max": float(np.max(logits)),
"logit_mean": float(np.mean(logits)),
},
}
path = OUT / "timercd_checkpoint_probe.json"
path.write_text(json.dumps(result, indent=2), encoding="utf-8")
print(json.dumps(result, indent=2))
print(f"wrote {path}")
if __name__ == "__main__":
main()
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