license: mit
Huaxiang ChaPanda Timeout Model
This repository contains the training and inference code for estimating whether Fujian Normal University Huaxiang Garden ChaPanda delivery orders may time out from the order time.
Data Assumption
records.csv in the Dataset repository stores the manually maintained base
records. When both timeout and non-timeout samples are present, retraining uses
labeled_empirical_bayes, which is closer to a calibrated probability model. If
only timeout samples are available, the artifact remains a positive-only
time-risk model.
Train
From the local conda environment:
conda activate cbd
python train.py --dataset-dir ..\FJNU-HuaxiangChaPandaTimeoutRecord
Or download from Hugging Face:
python train.py --download-from-hub
The default output is timeout_model.json.
Predict
python predict.py "2026-05-24 18:30"
The output includes the estimated probability, risk level, model mode, and a
calibration note. When the model mode is positive_only_time_density, treat the
number as a relative risk estimate until enough non-timeout samples are collected.