Rethinking-Anomaly-Detection
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This pull request introduces a series of improvements and code cleanups across multiple files, focusing on code style consistency, module import order, and enhanced documentation for the BWGNN implementation
This pull request introduces a series of improvements and code cleanups across multiple files, focusing on code style consistency, module import order, and enhanced documentation for the BWGNN implementation. The most important changes include a new, detailed README_FIXED.md documenting critical bug fixes and usage, consistent code style and import ordering, and improved clarity in dataset and training procedures.
Documentation and Usability Improvements
- Added a comprehensive
README_FIXED.mdthat explains critical issues fixed in the BWGNN implementation, including Laplacian computation, wavelet normalization, heterogeneous graph processing, training/eval separation, threshold selection, parameter registration, and batch dimension handling. It also provides installation, usage, configuration, testing, architecture, mathematical details, performance, file structure, logging, and reproducibility information.
Code Style and Import Order
- Standardized import ordering and formatting in
BWGNN.py,main.py, anddataset.pyto improve readability and maintainability. This includes grouping standard libraries, third-party libraries, and local modules, and using consistent quotation marks. [1] [2] [3]
Dataset Handling and Feature Processing
- Improved dataset loading and feature processing in
dataset.py: consistent use of double quotes, clearer logic for anomaly injection, and more robust handling of graph node features and labels.
Training and Evaluation Clarity
- Refactored the training loop and evaluation metrics in
main.pyfor clarity and consistency. This includes improved mask handling, clearer metric calculation, and more readable output formatting. [1] [2] [3] [4] [5]
Minor Code Quality Improvements
- Enhanced code style in Laplacian and wavelet computation functions in
BWGNN.py, including better formatting, clearer variable naming, and consistent use of double quotes. [1] [2] [3] [4]