Claude/traffic data diffusion model 01 agm qi pr web ef4bj n cu ge no #32
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This commit implements a novel implicit-explicit diffusion model for time series
imputation on the LD2011_2014 dataset.
Key Features:
- Implicit feature extraction using multi-scale dilated causal convolutions
- Explicit feature extraction using S4 state space models
- Fusion of implicit and explicit features for improved performance
- Support for training and evaluation at multiple missing ratios (20%-80%)
New Files:
1. src/imputers/ImplicitExplicitDiffusion.py
- Core model implementation with detailed Chinese comments (750 lines)
- ImplicitFeatureExtractor: Multi-scale dilated convolutions
- ExplicitFeatureExtractor: S4 state space model
- Residual blocks with feature fusion
2. src/data_loader_ld2011.py
- Data loading and preprocessing for LD2011_2014 dataset (260 lines)
- Flexible data parsing (semicolon/tab/comma separators)
- Automatic normalization and outlier handling
- Sliding window sequence generation
3. src/train_ld2011.py
- Training script with support for different missing ratios (350 lines)
- Automatic checkpoint saving and recovery
- Training loss visualization
- Multi-GPU support
4. src/evaluate_ld2011.py
- Evaluation script calculating MAE and RMSE metrics (450 lines)
- Support for single/multiple missing ratio evaluation
- Automatic visualization of imputation results
- CSV format evaluation report
5. src/config/config_ImplicitExplicit_LD2011.json
- Configuration file for LD2011 dataset experiments
- Diffusion config: T=200, beta_0=0.0001, beta_T=0.02
- Model config: 14 channels, 256 res_channels, 36 layers
- Implicit module: dilation_rates=[1,2,4,8,16]
- Explicit module: S4 lmax=100, d_state=64
6. run_experiments.sh
- Automated experiment script (200 lines)
- Complete pipeline: preprocessing -> training -> evaluation
- GPU status checking and error handling
- Colored output and progress tracking
7. README_ImplicitExplicitDiffusion.md
- Comprehensive documentation (1500 lines)
- Project overview and architecture explanation
- Environment setup guide
- Three-level hyperparameter tuning guide:
* Basic: learning rate, batch size, iterations
* Intermediate: model capacity (channels, layers)
* Advanced: module architecture (dilation rates, S4 parameters)
- Code structure and detailed explanation
- FAQ (10 common questions)
8. QUICKSTART.md
- 5-minute quick start guide (400 lines)
- Environment installation steps
- Data preparation guide
- Training and evaluation examples
- Quick troubleshooting
9. PROJECT_SUMMARY.md
- Project implementation summary
- Code statistics and highlights
- Learning objectives achievement
- Technical stack and key techniques
Code Quality:
- Every line of code has detailed Chinese comments
- Modular design with clear separation of concerns
- Three-tier documentation: code comments + quickstart + detailed README
- Support for three learning levels:
* Basic: Hyperparameter tuning
* Intermediate: Module architecture adjustment
* Advanced: Diffusion model optimization
Target Metrics (from paper):
- 20% missing: MAE 0.272, RMSE 0.389
- 30% missing: MAE 0.297, RMSE 0.424
- 40% missing: MAE 0.334, RMSE 0.477
- 50% missing: MAE 0.378, RMSE 0.540
- 60% missing: MAE 0.450, RMSE 0.655
- 70% missing: MAE 0.541, RMSE 0.776
- 80% missing: MAE 0.732, RMSE 1.049
Total: ~4000 lines of code and documentation
Tested: Code syntax validated, ready for deployment testing
- check_environment.py: Comprehensive environment validation script - setup_config.sh: Automated setup wizard for data path and GPU configuration - config_ImplicitExplicit_LD2011_QuickTest.json: Quick test config (5000 iters, smaller model) - INSTALLATION_GUIDE.md: Complete installation and usage guide for AnYujin environment These tools help users: - Verify all dependencies are correctly installed - Automatically configure data paths and GPU settings - Run quick tests before full training - Follow step-by-step installation instructions
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