- Make FAISS import optional with try/except
- CDCPreprocessor raises helpful ImportError if FAISS unavailable
- train_util.py catches ImportError and returns None
- train_network.py checks for None and warns user
- Training continues without CDC-FM if FAISS not installed
- Remove benchmark file (not needed in repo)
This allows users to run training without FAISS dependency.
CDC-FM will be automatically disabled with a warning if FAISS is missing.
- Add --cdc_adaptive_k flag to enable adaptive k based on bucket size
- Add --cdc_min_bucket_size to set minimum bucket threshold (default: 16)
- Fixed mode (default): Skip buckets with < k_neighbors samples
- Adaptive mode: Use k=min(k_neighbors, bucket_size-1) for buckets >= min_bucket_size
- Update CDCPreprocessor to support adaptive k per bucket
- Add metadata tracking for adaptive_k and min_bucket_size
- Add comprehensive pytest tests for adaptive k behavior
This allows CDC-FM to work effectively with multi-resolution bucketing where
bucket sizes may vary widely. Users can choose between strict paper methodology
(fixed k) or pragmatic approach (adaptive k).
- Add ss_use_cdc_fm, ss_cdc_k_neighbors, ss_cdc_k_bandwidth, ss_cdc_d_cdc, ss_cdc_gamma
- Ensures CDC-FM training parameters are tracked in model metadata
- Enables reproducibility and model provenance tracking
- Add --cdc_debug flag to enable verbose bucket-by-bucket output
- When debug=False (default): Show tqdm progress bar, concise logging
- When debug=True: Show detailed bucket information, no progress bar
- Improves user experience during CDC cache generation
