docs/notebooks.md

Notebook Guide

Execution Order

Run the notebooks sequentially — each builds on artifacts from previous ones. All 9 notebooks have been fully executed and validated.

#	Notebook	Purpose	Key Results
01	01_eda.ipynb	Exploratory Data Analysis	30 batteries, 2678 cycles, 5 temp groups
02	02_feature_engineering.ipynb	Feature extraction + sliding windows	2678×19 features, 1734 sequences (32×12)
03	03_classical_ml.ipynb	Classical ML (Optuna HPO)	RF R²=0.957 (best overall), XGB RUL R²=0.536
04	04_lstm_rnn.ipynb	LSTM/GRU family (CUDA)	Vanilla LSTM R²=0.507 best of family
05	05_transformer.ipynb	BatteryGPT, TFT, iTransformer	TFT R²=0.881 (best DL)
06	06_dynamic_graph.ipynb	Dynamic-Graph iTransformer	DG-iTransformer R²=0.595
07	07_vae_lstm.ipynb	VAE-LSTM + anomaly detection	R²=0.730, UMAP latent space
08	08_ensemble.ipynb	Stacking & Weighted Average	Weighted Avg R²=0.886 (TFT weight=93.5%)
09	09_evaluation.ipynb	Unified comparison & recommendations	22 models ranked, RF champion

Artifacts Produced

Notebook	Files
02	battery_features.csv, battery_sequences.npz, scalers
03	.joblib models, classical_soh_results.csv, classical_rul_results.csv
04	.pt checkpoints (vanilla/bi/gru/attention LSTM), lstm_soh_results.csv
05	.pt (BatteryGPT, TFT), .keras (iTransformer), transformer_soh_results.csv
06	.keras checkpoint, dg_itransformer_results.json
07	vae_lstm.pt, vae_lstm_results.json, UMAP plots
08	ensemble_results.csv, weight/comparison plots
09	unified_results.csv, final_rankings.csv, radar/CED/comparison plots

Key Dependencies

• Notebook 02 produces battery_sequences.npz (sliding windows) used by all deep learning notebooks
• Notebook 02 produces battery_features.csv used by notebook 03
• Notebooks 04-07 produce model checkpoints used by notebook 08 (ensemble)
• All result CSVs are consumed by notebook 09 for unified evaluation

GPU Notes

• PyTorch notebooks (04, 07, 08) use CUDA when available (torch.cuda.is_available())
• TensorFlow/Keras notebooks (05, 06) run CPU-only on Windows (no native TF GPU support)
• Classical ML (notebook 03) is CPU-only, completes in ~2-5 minutes
• Deep learning notebooks: ~2-10 min on GPU, ~15-60 min on CPU