File size: 13,627 Bytes
8b37c3f cbba70a 8b37c3f cbba70a 8b37c3f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 | """Generate the ordered, leakage-aware v3 review notebooks."""
from __future__ import annotations
import json
from pathlib import Path
ROOT = Path(__file__).resolve().parents[2]
NOTEBOOKS = ROOT / "notebooks"
SETUP = """from pathlib import Path
import sys
PROJECT_ROOT = Path.cwd().resolve()
if PROJECT_ROOT.name == "notebooks":
PROJECT_ROOT = PROJECT_ROOT.parent
if str(PROJECT_ROOT) not in sys.path:
sys.path.insert(0, str(PROJECT_ROOT))
ARTIFACT_ROOT = PROJECT_ROOT / "artifacts" / "v3"
RESULTS = ARTIFACT_ROOT / "results"
FIGURES = ARTIFACT_ROOT / "figures"
FEATURES = ARTIFACT_ROOT / "features"
for path in (RESULTS, FIGURES, FEATURES):
path.mkdir(parents=True, exist_ok=True)
print(f"Project root: {PROJECT_ROOT}")
"""
def md(text: str) -> dict:
return {"cell_type": "markdown", "metadata": {}, "source": text.splitlines(keepends=True)}
def py(text: str) -> dict:
return {
"cell_type": "code",
"execution_count": None,
"metadata": {},
"outputs": [],
"source": text.splitlines(keepends=True),
}
def make_notebook(title: str, purpose: str, cells: list[dict]) -> dict:
intro = f"""# {title}
Purpose: {purpose}
Leakage rule: current-cycle full discharged capacity and SOH are labels only.
Preprocessing is fitted on training batteries, validation batteries control
selection and early stopping, and test batteries are scored exactly once.
"""
notebook_cells = [md(intro), py(SETUP), *cells]
for index, cell in enumerate(notebook_cells):
cell["id"] = f"cell-{index:02d}"
return {
"cells": notebook_cells,
"metadata": {
"kernelspec": {
"display_name": "Python 3 (aiBatteryLifecycle)",
"language": "python",
"name": "python3",
},
"language_info": {"name": "python", "version": "3.12"},
"review_protocol": "v3-leakage-aware-2026-09",
},
"nbformat": 4,
"nbformat_minor": 5,
}
SPECS = {
"00_environment.ipynb": (
"00 — Environment and Reproducibility",
"Record software, hardware, seeds, source checksums, and the exact 20-model registry.",
[
md("## Fixed protocol\n\nSeeds: 17, 42, 2026. Primary criterion: macro per-battery MAE; tie-breakers: RMSE, serialized size, then CPU latency."),
py("""import json, os, platform
import numpy as np, pandas as pd, sklearn, scipy, psutil, torch, tensorflow as tf
import xgboost, lightgbm
from src.models.catalog import model_catalog_records
environment = {
"python": platform.python_version(),
"platform": platform.platform(),
"processor": platform.processor(),
"logical_cpu_count": os.cpu_count(),
"ram_gib": round(psutil.virtual_memory().total / 1024**3, 2),
"cuda_available": torch.cuda.is_available(),
"numpy": np.__version__,
"pandas": pd.__version__,
"scikit_learn": sklearn.__version__,
"scipy": scipy.__version__,
"pytorch": torch.__version__,
"tensorflow": tf.__version__,
"xgboost": xgboost.__version__,
"lightgbm": lightgbm.__version__,
"seeds": [17, 42, 2026],
"primary_metric": "macro_per_battery_mae",
}
(RESULTS / "environment.json").write_text(json.dumps(environment, indent=2), encoding="utf-8")
catalog = pd.DataFrame(model_catalog_records())
catalog.to_csv(RESULTS / "model_catalog.csv", index=False)
display(catalog)
environment"""),
py("""checksum_file = PROJECT_ROOT / "datasets" / "checksums.sha256"
print(checksum_file.read_text(encoding="utf-8") if checksum_file.exists() else "Run the benchmark downloader first.")"""),
],
),
"01_dataset_audit.ipynb": (
"01 — Dataset Audit",
"Inventory NASA, CALCE, and Oxford cells, cycles, conditions, missing channels, exclusions, and provenance.",
[
md("## Normalize raw cycles\n\nThis step engineers no predictors. Every exclusion is recorded with a reason."),
py("""import pandas as pd
from scripts.data.build_benchmark_datasets import load_all_cycles
from src.data.adapters import cycle_inventory
cycles_by_dataset = load_all_cycles(PROJECT_ROOT / "datasets" / "raw")
inventories = {}
for name, cycles in cycles_by_dataset.items():
inventory = cycle_inventory(cycles)
inventory.to_csv(FEATURES / f"{name.lower()}_inventory.csv", index=False)
inventories[name] = inventory
display(inventory.groupby("battery_id").agg(
cycles=("cycle_number", "count"),
capacity_min_ah=("capacity_ah", "min"),
capacity_max_ah=("capacity_ah", "max"),
))
audit = pd.concat(inventories.values(), ignore_index=True)
display(audit.groupby("dataset").agg(
batteries=("battery_id", "nunique"),
cycles=("cycle_number", "count"),
measurements=("n_measurements", "sum"),
))"""),
],
),
"02_feature_engineering.ipynb": (
"02 — Leakage-Safe Partial-Cycle Features",
"Build 18 scalar predictors and 64 x 5 sequences from the observable 4.0–3.6 V discharge segment.",
[
md("## Target and inputs\n\nSOH = 100 x Q_t / median(Q_1,Q_2,Q_3). Full-cycle Q_t is the target only; inputs use the partial voltage window and prior usage."),
py("""import pandas as pd
from scripts.data.build_benchmark_datasets import build_all_benchmarks
summaries = build_all_benchmarks(PROJECT_ROOT / "datasets" / "raw", FEATURES)
pd.DataFrame(summaries)"""),
py("""from src.utils.config import FEATURE_COLS_V3
forbidden = {"Capacity", "capacity_ah", "SoH", "delta_capacity", "soh_rolling_mean"}
assert not forbidden.intersection(FEATURE_COLS_V3)
print(f"Verified {len(FEATURE_COLS_V3)} predictors with no current-cycle target proxies.")"""),
],
),
"03_protocol_comparison.ipynb": (
"03 — Quantitative V1/V2/V3 Protocol Comparison",
"Separate split-design effects from feature-leakage effects with a factorial comparison.",
[
md("## Design\n\nV1: legacy random-cycle holdout. V2: within-battery chronological 80/20. V3: five-fold battery-grouped validation. Legacy and safe feature sets are crossed where possible."),
py("""from scripts.run_protocol_comparison import run_protocol_comparison
comparison = run_protocol_comparison(PROJECT_ROOT, seeds=(17, 42, 2026))
comparison.to_csv(RESULTS / "protocol_comparison.csv", index=False)
display(comparison)"""),
],
),
"04_classical_ml.ipynb": (
"04 — Classical Models",
"Evaluate eight classical regressors under grouped nested validation.",
[
py("""import pandas as pd
from src.experiments.classical import run_grouped_tabular_benchmark
nasa = pd.read_csv(FEATURES / "nasa" / "features.csv")
metric_path = RESULTS / "nasa_classical_fold_metrics.csv"
prediction_path = RESULTS / "nasa_classical_predictions.csv"
if not metric_path.exists() or not prediction_path.exists():
metrics, predictions = run_grouped_tabular_benchmark(nasa, dataset_name="NASA")
metrics.to_csv(metric_path, index=False)
predictions.to_csv(prediction_path, index=False)
else:
metrics = pd.read_csv(metric_path)
display(metrics.groupby("model")[["mae", "rmse", "r2", "within_5pp"]].mean().sort_values("mae"))"""),
],
),
"05_recurrent_models.ipynb": (
"05 — Recurrent Models",
"Evaluate Vanilla LSTM, Bidirectional LSTM, GRU, and Attention LSTM with validation-battery early stopping.",
[
py("""import numpy as np, pandas as pd
from src.experiments.deep import run_grouped_sequence_benchmark
data = np.load(FEATURES / "nasa" / "sequences.npz")
index = pd.read_csv(FEATURES / "nasa" / "sequence_index.csv")
models = ("vanilla_lstm", "bidirectional_lstm", "gru", "attention_lstm")
metric_path = RESULTS / "nasa_recurrent_fold_metrics.csv"
prediction_path = RESULTS / "nasa_recurrent_predictions.csv"
if not metric_path.exists() or not prediction_path.exists():
metrics, predictions = run_grouped_sequence_benchmark(data["X"], index, dataset_name="NASA", model_ids=models)
metrics.to_csv(metric_path, index=False)
predictions.to_csv(prediction_path, index=False)
else:
metrics = pd.read_csv(metric_path)
display(metrics.groupby("model")[["mae", "rmse", "r2", "within_5pp"]].mean().sort_values("mae"))"""),
],
),
"06_transformer_models.ipynb": (
"06 — Transformer Models",
"Evaluate BatteryGPT, TFT, iTransformer, and physics-informed iTransformer.",
[
py("""import numpy as np, pandas as pd
from src.experiments.deep import run_grouped_sequence_benchmark
data = np.load(FEATURES / "nasa" / "sequences.npz")
index = pd.read_csv(FEATURES / "nasa" / "sequence_index.csv")
models = ("battery_gpt", "temporal_fusion_transformer", "itransformer", "physics_itransformer")
metric_path = RESULTS / "nasa_transformer_fold_metrics.csv"
prediction_path = RESULTS / "nasa_transformer_predictions.csv"
if not metric_path.exists() or not prediction_path.exists():
metrics, predictions = run_grouped_sequence_benchmark(data["X"], index, dataset_name="NASA", model_ids=models)
metrics.to_csv(metric_path, index=False)
predictions.to_csv(prediction_path, index=False)
else:
metrics = pd.read_csv(metric_path)
display(metrics.groupby("model")[["mae", "rmse", "r2", "within_5pp"]].mean().sort_values("mae"))"""),
],
),
"07_graph_variational.ipynb": (
"07 — Graph and Variational Models",
"Evaluate Dynamic-Graph iTransformer and VAE-LSTM on the same untouched test batteries.",
[
py("""import numpy as np, pandas as pd
from src.experiments.deep import run_grouped_sequence_benchmark
data = np.load(FEATURES / "nasa" / "sequences.npz")
index = pd.read_csv(FEATURES / "nasa" / "sequence_index.csv")
models = ("dynamic_graph_itransformer", "vae_lstm")
metric_path = RESULTS / "nasa_graph_variational_fold_metrics.csv"
prediction_path = RESULTS / "nasa_graph_variational_predictions.csv"
if not metric_path.exists() or not prediction_path.exists():
metrics, predictions = run_grouped_sequence_benchmark(data["X"], index, dataset_name="NASA", model_ids=models)
metrics.to_csv(metric_path, index=False)
predictions.to_csv(prediction_path, index=False)
else:
metrics = pd.read_csv(metric_path)
display(metrics.groupby("model")[["mae", "rmse", "r2", "within_5pp"]].mean().sort_values("mae"))"""),
],
),
"08_ensembles.ipynb": (
"08 — Leakage-Safe Ensembles",
"Review stacking and validation-error weighted ensembles fitted without outer-test labels.",
[
md("Ensemble predictions are produced in Notebook 04 so all base and meta models share the same outer test fold. Training MAE is an in-sample diagnostic; validation batteries, not training or test batteries, determine ensemble weights and stacking coefficients."),
py("""import pandas as pd
metrics = pd.read_csv(RESULTS / "nasa_classical_fold_metrics.csv")
ensemble = metrics[metrics["model"].isin(["stacking_ensemble", "weighted_ensemble"])]
display(ensemble.groupby("model")[["train_mae", "mae", "generalization_gap_mae", "rmse", "r2", "within_5pp"]].mean().sort_values("mae"))"""),
],
),
"09_external_calce.ipynb": (
"09 — CALCE External Validation",
"Run all 20 models in grouped CALCE validation and NASA-trained models zero-shot on CALCE.",
[
py("""from scripts.run_external_validation import run_external_validation
summary = run_external_validation(PROJECT_ROOT, dataset="calce", seeds=(17, 42, 2026))
summary.to_csv(RESULTS / "calce_validation_summary.csv", index=False)
display(summary)"""),
],
),
"10_external_oxford.ipynb": (
"10 — Oxford External Validation",
"Run all 20 models in grouped Oxford validation and NASA-trained models zero-shot on Oxford.",
[
py("""from scripts.run_external_validation import run_external_validation
summary = run_external_validation(PROJECT_ROOT, dataset="oxford", seeds=(17, 42, 2026))
summary.to_csv(RESULTS / "oxford_validation_summary.csv", index=False)
display(summary)"""),
],
),
"11_stats_robustness.ipynb": (
"11 — Statistics, Residuals, Robustness, and Ablations",
"Compute cluster-bootstrap intervals, Wilcoxon-Holm tests, residual diagnostics, sensor stress tests, and feature ablations.",
[
py("""from scripts.run_statistical_analysis import run_statistical_analysis
outputs = run_statistical_analysis(PROJECT_ROOT, bootstrap_samples=10_000)
for name, table in outputs.items():
display(name, table.head())"""),
],
),
"12_paper_outputs.ipynb": (
"12 — Publication Tables and Figures",
"Generate every manuscript table and high-resolution figure from verified machine-readable outputs.",
[
py("""from scripts.generate_paper_outputs import generate_paper_outputs
manifest = generate_paper_outputs(PROJECT_ROOT)
display(manifest)"""),
md("Raster figures are exported at 600 dpi and line plots also as PDF/SVG. The manuscript uses consistent Arabic numbering: Table 1, Table 2, and so on."),
],
),
}
def main() -> None:
NOTEBOOKS.mkdir(parents=True, exist_ok=True)
for filename, (title, purpose, cells) in SPECS.items():
path = NOTEBOOKS / filename
path.write_text(json.dumps(make_notebook(title, purpose, cells), indent=1), encoding="utf-8")
print(f"Wrote {path.relative_to(ROOT)}")
if __name__ == "__main__":
main()
|