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FJNU-HuaxiangChaPandaTimeoutModel

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Add precomputed peak forecast artifact

Browse files
Files changed (4) hide show
  1. README.md +12 -0
  2. generate_peak_forecast.py +79 -0
  3. peak_forecast_default.json +4079 -0
  4. train.py +12 -0
README.md CHANGED
@@ -32,6 +32,18 @@ python train.py --download-from-hub
32
 
33
  The default output is `timeout_model.json`.
34
 
 
 
 
 
 
 
 
 
 
 
 
 
35
  ## Predict
36
 
37
  ```powershell
 
32
 
33
  The default output is `timeout_model.json`.
34
 
35
+ ## Peak Forecast Artifact
36
+
37
+ Generate the default, product-agnostic 10-minute peak forecast used by the Space:
38
+
39
+ ```powershell
40
+ python generate_peak_forecast.py
41
+ ```
42
+
43
+ The output is `peak_forecast_default.json`. It stores one precomputed curve for
44
+ each weekday, so the Space can render the default peak chart without recomputing
45
+ 144 prediction points on every page load.
46
+
47
  ## Predict
48
 
49
  ```powershell
generate_peak_forecast.py ADDED
@@ -0,0 +1,79 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from __future__ import annotations
2
+
3
+ import argparse
4
+ import json
5
+ from datetime import datetime, timedelta, timezone
6
+ from pathlib import Path
7
+ from typing import Any
8
+
9
+ from timeout_risk_model import load_model, predict_timeout_probability
10
+
11
+
12
+ WEEKDAY_ANCHORS = {
13
+ 0: "2026-05-25",
14
+ 1: "2026-05-26",
15
+ 2: "2026-05-27",
16
+ 3: "2026-05-28",
17
+ 4: "2026-05-29",
18
+ 5: "2026-05-30",
19
+ 6: "2026-05-31",
20
+ }
21
+
22
+
23
+ def build_weekday_forecast(model: dict[str, Any], weekday: int, interval_minutes: int) -> dict[str, Any]:
24
+ day_start = datetime.strptime(WEEKDAY_ANCHORS[weekday], "%Y-%m-%d")
25
+ points = []
26
+ for minute in range(0, 24 * 60, interval_minutes):
27
+ point_time = day_start + timedelta(minutes=minute)
28
+ result = predict_timeout_probability(model, point_time)
29
+ points.append(
30
+ {
31
+ "time": point_time.strftime("%H:%M"),
32
+ "probability": round(float(result["probability"]) * 100, 1),
33
+ }
34
+ )
35
+ return {"anchor_date": WEEKDAY_ANCHORS[weekday], "points": points}
36
+
37
+
38
+ def build_forecast(model_path: Path, interval_minutes: int) -> dict[str, Any]:
39
+ model = load_model(model_path)
40
+ return {
41
+ "schema_version": 1,
42
+ "product_name": "",
43
+ "unit": "percent",
44
+ "interval_minutes": interval_minutes,
45
+ "model_version": model.get("model_version"),
46
+ "model_mode": model.get("mode"),
47
+ "model_generated_at": model.get("generated_at"),
48
+ "generated_at": datetime.now(timezone.utc).isoformat(),
49
+ "weekday_forecasts": {
50
+ str(weekday): build_weekday_forecast(model, weekday, interval_minutes)
51
+ for weekday in range(7)
52
+ },
53
+ }
54
+
55
+
56
+ def main() -> None:
57
+ parser = argparse.ArgumentParser(description="Generate precomputed peak forecast curves.")
58
+ parser.add_argument(
59
+ "--model",
60
+ type=Path,
61
+ default=Path(__file__).resolve().parent / "timeout_model.json",
62
+ help="Path to timeout_model.json.",
63
+ )
64
+ parser.add_argument(
65
+ "--output",
66
+ type=Path,
67
+ default=Path(__file__).resolve().parent / "peak_forecast_default.json",
68
+ help="Path for generated peak forecast JSON.",
69
+ )
70
+ parser.add_argument("--interval-minutes", type=int, default=10)
71
+ args = parser.parse_args()
72
+
73
+ forecast = build_forecast(args.model, args.interval_minutes)
74
+ args.output.write_text(json.dumps(forecast, ensure_ascii=False, indent=2) + "\n", encoding="utf-8")
75
+ print(f"Wrote {args.output}")
76
+
77
+
78
+ if __name__ == "__main__":
79
+ main()
peak_forecast_default.json ADDED
@@ -0,0 +1,4079 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "schema_version": 1,
3
+ "product_name": "",
4
+ "unit": "percent",
5
+ "interval_minutes": 10,
6
+ "model_version": 1,
7
+ "model_mode": "positive_only_time_density",
8
+ "model_generated_at": "2026-05-23T18:07:51.470272+00:00",
9
+ "generated_at": "2026-05-24T10:01:41.847705+00:00",
10
+ "weekday_forecasts": {
11
+ "0": {
12
+ "anchor_date": "2026-05-25",
13
+ "points": [
14
+ {
15
+ "time": "00:00",
16
+ "probability": 13.3
17
+ },
18
+ {
19
+ "time": "00:10",
20
+ "probability": 13.3
21
+ },
22
+ {
23
+ "time": "00:20",
24
+ "probability": 13.3
25
+ },
26
+ {
27
+ "time": "00:30",
28
+ "probability": 13.3
29
+ },
30
+ {
31
+ "time": "00:40",
32
+ "probability": 13.3
33
+ },
34
+ {
35
+ "time": "00:50",
36
+ "probability": 13.3
37
+ },
38
+ {
39
+ "time": "01:00",
40
+ "probability": 13.3
41
+ },
42
+ {
43
+ "time": "01:10",
44
+ "probability": 13.3
45
+ },
46
+ {
47
+ "time": "01:20",
48
+ "probability": 13.3
49
+ },
50
+ {
51
+ "time": "01:30",
52
+ "probability": 13.3
53
+ },
54
+ {
55
+ "time": "01:40",
56
+ "probability": 13.3
57
+ },
58
+ {
59
+ "time": "01:50",
60
+ "probability": 13.3
61
+ },
62
+ {
63
+ "time": "02:00",
64
+ "probability": 13.3
65
+ },
66
+ {
67
+ "time": "02:10",
68
+ "probability": 13.3
69
+ },
70
+ {
71
+ "time": "02:20",
72
+ "probability": 13.3
73
+ },
74
+ {
75
+ "time": "02:30",
76
+ "probability": 13.3
77
+ },
78
+ {
79
+ "time": "02:40",
80
+ "probability": 13.3
81
+ },
82
+ {
83
+ "time": "02:50",
84
+ "probability": 13.3
85
+ },
86
+ {
87
+ "time": "03:00",
88
+ "probability": 13.3
89
+ },
90
+ {
91
+ "time": "03:10",
92
+ "probability": 13.3
93
+ },
94
+ {
95
+ "time": "03:20",
96
+ "probability": 13.3
97
+ },
98
+ {
99
+ "time": "03:30",
100
+ "probability": 13.3
101
+ },
102
+ {
103
+ "time": "03:40",
104
+ "probability": 13.3
105
+ },
106
+ {
107
+ "time": "03:50",
108
+ "probability": 13.3
109
+ },
110
+ {
111
+ "time": "04:00",
112
+ "probability": 13.3
113
+ },
114
+ {
115
+ "time": "04:10",
116
+ "probability": 13.3
117
+ },
118
+ {
119
+ "time": "04:20",
120
+ "probability": 13.3
121
+ },
122
+ {
123
+ "time": "04:30",
124
+ "probability": 13.3
125
+ },
126
+ {
127
+ "time": "04:40",
128
+ "probability": 13.3
129
+ },
130
+ {
131
+ "time": "04:50",
132
+ "probability": 13.3
133
+ },
134
+ {
135
+ "time": "05:00",
136
+ "probability": 13.3
137
+ },
138
+ {
139
+ "time": "05:10",
140
+ "probability": 13.3
141
+ },
142
+ {
143
+ "time": "05:20",
144
+ "probability": 13.3
145
+ },
146
+ {
147
+ "time": "05:30",
148
+ "probability": 13.3
149
+ },
150
+ {
151
+ "time": "05:40",
152
+ "probability": 13.3
153
+ },
154
+ {
155
+ "time": "05:50",
156
+ "probability": 13.3
157
+ },
158
+ {
159
+ "time": "06:00",
160
+ "probability": 13.3
161
+ },
162
+ {
163
+ "time": "06:10",
164
+ "probability": 13.3
165
+ },
166
+ {
167
+ "time": "06:20",
168
+ "probability": 13.3
169
+ },
170
+ {
171
+ "time": "06:30",
172
+ "probability": 13.3
173
+ },
174
+ {
175
+ "time": "06:40",
176
+ "probability": 13.3
177
+ },
178
+ {
179
+ "time": "06:50",
180
+ "probability": 13.3
181
+ },
182
+ {
183
+ "time": "07:00",
184
+ "probability": 13.3
185
+ },
186
+ {
187
+ "time": "07:10",
188
+ "probability": 13.3
189
+ },
190
+ {
191
+ "time": "07:20",
192
+ "probability": 13.3
193
+ },
194
+ {
195
+ "time": "07:30",
196
+ "probability": 13.3
197
+ },
198
+ {
199
+ "time": "07:40",
200
+ "probability": 13.3
201
+ },
202
+ {
203
+ "time": "07:50",
204
+ "probability": 13.3
205
+ },
206
+ {
207
+ "time": "08:00",
208
+ "probability": 13.3
209
+ },
210
+ {
211
+ "time": "08:10",
212
+ "probability": 13.3
213
+ },
214
+ {
215
+ "time": "08:20",
216
+ "probability": 13.3
217
+ },
218
+ {
219
+ "time": "08:30",
220
+ "probability": 13.3
221
+ },
222
+ {
223
+ "time": "08:40",
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3927
+ },
3928
+ {
3929
+ "time": "17:50",
3930
+ "probability": 71.9
3931
+ },
3932
+ {
3933
+ "time": "18:00",
3934
+ "probability": 66.1
3935
+ },
3936
+ {
3937
+ "time": "18:10",
3938
+ "probability": 66.1
3939
+ },
3940
+ {
3941
+ "time": "18:20",
3942
+ "probability": 66.1
3943
+ },
3944
+ {
3945
+ "time": "18:30",
3946
+ "probability": 66.1
3947
+ },
3948
+ {
3949
+ "time": "18:40",
3950
+ "probability": 66.1
3951
+ },
3952
+ {
3953
+ "time": "18:50",
3954
+ "probability": 66.1
3955
+ },
3956
+ {
3957
+ "time": "19:00",
3958
+ "probability": 39.9
3959
+ },
3960
+ {
3961
+ "time": "19:10",
3962
+ "probability": 39.9
3963
+ },
3964
+ {
3965
+ "time": "19:20",
3966
+ "probability": 39.9
3967
+ },
3968
+ {
3969
+ "time": "19:30",
3970
+ "probability": 39.9
3971
+ },
3972
+ {
3973
+ "time": "19:40",
3974
+ "probability": 39.9
3975
+ },
3976
+ {
3977
+ "time": "19:50",
3978
+ "probability": 39.9
3979
+ },
3980
+ {
3981
+ "time": "20:00",
3982
+ "probability": 17.4
3983
+ },
3984
+ {
3985
+ "time": "20:10",
3986
+ "probability": 17.4
3987
+ },
3988
+ {
3989
+ "time": "20:20",
3990
+ "probability": 17.4
3991
+ },
3992
+ {
3993
+ "time": "20:30",
3994
+ "probability": 17.4
3995
+ },
3996
+ {
3997
+ "time": "20:40",
3998
+ "probability": 17.4
3999
+ },
4000
+ {
4001
+ "time": "20:50",
4002
+ "probability": 17.4
4003
+ },
4004
+ {
4005
+ "time": "21:00",
4006
+ "probability": 13.3
4007
+ },
4008
+ {
4009
+ "time": "21:10",
4010
+ "probability": 13.3
4011
+ },
4012
+ {
4013
+ "time": "21:20",
4014
+ "probability": 13.3
4015
+ },
4016
+ {
4017
+ "time": "21:30",
4018
+ "probability": 13.3
4019
+ },
4020
+ {
4021
+ "time": "21:40",
4022
+ "probability": 13.3
4023
+ },
4024
+ {
4025
+ "time": "21:50",
4026
+ "probability": 13.3
4027
+ },
4028
+ {
4029
+ "time": "22:00",
4030
+ "probability": 13.3
4031
+ },
4032
+ {
4033
+ "time": "22:10",
4034
+ "probability": 13.3
4035
+ },
4036
+ {
4037
+ "time": "22:20",
4038
+ "probability": 13.3
4039
+ },
4040
+ {
4041
+ "time": "22:30",
4042
+ "probability": 13.3
4043
+ },
4044
+ {
4045
+ "time": "22:40",
4046
+ "probability": 13.3
4047
+ },
4048
+ {
4049
+ "time": "22:50",
4050
+ "probability": 13.3
4051
+ },
4052
+ {
4053
+ "time": "23:00",
4054
+ "probability": 13.3
4055
+ },
4056
+ {
4057
+ "time": "23:10",
4058
+ "probability": 13.3
4059
+ },
4060
+ {
4061
+ "time": "23:20",
4062
+ "probability": 13.3
4063
+ },
4064
+ {
4065
+ "time": "23:30",
4066
+ "probability": 13.3
4067
+ },
4068
+ {
4069
+ "time": "23:40",
4070
+ "probability": 13.3
4071
+ },
4072
+ {
4073
+ "time": "23:50",
4074
+ "probability": 13.3
4075
+ }
4076
+ ]
4077
+ }
4078
+ }
4079
+ }
train.py CHANGED
@@ -1,10 +1,12 @@
1
  from __future__ import annotations
2
 
3
  import argparse
 
4
  import shutil
5
  import tempfile
6
  from pathlib import Path
7
 
 
8
  from timeout_risk_model import load_dataset_records, save_model, train_timeout_model
9
 
10
  DEFAULT_DATASET_REPO_ID = "stack-all/FJNU-HuaxiangChaPandaTimeoutRecord"
@@ -54,6 +56,12 @@ def parse_args() -> argparse.Namespace:
54
  default=Path(__file__).resolve().parent / "timeout_model.json",
55
  help="Output JSON model path.",
56
  )
 
 
 
 
 
 
57
  return parser.parse_args()
58
 
59
 
@@ -70,12 +78,16 @@ def main() -> None:
70
  records = load_dataset_records(dataset_dir)
71
  model = train_timeout_model(records)
72
  save_model(model, args.output)
 
 
 
73
  finally:
74
  if downloaded_dir:
75
  shutil.rmtree(downloaded_dir, ignore_errors=True)
76
 
77
  metadata = model["metadata"]
78
  print(f"Saved model: {args.output}")
 
79
  print(f"Mode: {model['mode']}")
80
  print(f"Training records: {metadata['training_records']}")
81
  print(f"Timeout / non-timeout: {metadata['timeout_records']} / {metadata['non_timeout_records']}")
 
1
  from __future__ import annotations
2
 
3
  import argparse
4
+ import json
5
  import shutil
6
  import tempfile
7
  from pathlib import Path
8
 
9
+ from generate_peak_forecast import build_forecast
10
  from timeout_risk_model import load_dataset_records, save_model, train_timeout_model
11
 
12
  DEFAULT_DATASET_REPO_ID = "stack-all/FJNU-HuaxiangChaPandaTimeoutRecord"
 
56
  default=Path(__file__).resolve().parent / "timeout_model.json",
57
  help="Output JSON model path.",
58
  )
59
+ parser.add_argument(
60
+ "--peak-output",
61
+ type=Path,
62
+ default=Path(__file__).resolve().parent / "peak_forecast_default.json",
63
+ help="Output JSON path for the precomputed product-agnostic peak forecast.",
64
+ )
65
  return parser.parse_args()
66
 
67
 
 
78
  records = load_dataset_records(dataset_dir)
79
  model = train_timeout_model(records)
80
  save_model(model, args.output)
81
+ peak_forecast = build_forecast(args.output, interval_minutes=10)
82
+ args.peak_output.parent.mkdir(parents=True, exist_ok=True)
83
+ args.peak_output.write_text(json.dumps(peak_forecast, ensure_ascii=False, indent=2) + "\n", encoding="utf-8")
84
  finally:
85
  if downloaded_dir:
86
  shutil.rmtree(downloaded_dir, ignore_errors=True)
87
 
88
  metadata = model["metadata"]
89
  print(f"Saved model: {args.output}")
90
+ print(f"Saved peak forecast: {args.peak_output}")
91
  print(f"Mode: {model['mode']}")
92
  print(f"Training records: {metadata['training_records']}")
93
  print(f"Timeout / non-timeout: {metadata['timeout_records']} / {metadata['non_timeout_records']}")