| 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: | |
| ```powershell | |
| conda activate cbd | |
| python train.py --dataset-dir ..\FJNU-HuaxiangChaPandaTimeoutRecord | |
| ``` | |
| Or download from Hugging Face: | |
| ```powershell | |
| python train.py --download-from-hub | |
| ``` | |
| The default output is `timeout_model.json`. | |
| ## Predict | |
| ```powershell | |
| 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. | |