This is the example code for the Wyze rule recommendation challenge hosted in HF (https://huggingface.co/spaces/competitions/wyze-rule-recommendation), which reproduces the GraphRule algorithm for this dataset. The GraphRule is the centralized training of FedRule. This is only for demonstration purposes and serves as a simple baseline model. We do not perform any hyperparameter optimization. The key steps implemented here include:
- Loading and preprocessing the Wyze rule and device datasets
- Constructing user-rule and user-device graphs from the data
- Applying graph neural network propagation and embedding techniques
- Training a model on the centralized graph embeddings
- Using the model to predict missing rules for new users
This is intended as a sample starter code to illustrate one modeling approach for the competition. There are many other innovative modeling techniques that could be applied to effectively recommend personalized rules on this dataset.
python data_preprocess.py
python main.py
python output_result