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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 | """Within-dataset and NASA-to-external validation for CALCE or Oxford."""
from __future__ import annotations
import argparse
from pathlib import Path
import sys
import numpy as np
import pandas as pd
PROJECT_ROOT = Path(__file__).resolve().parents[1]
if str(PROJECT_ROOT) not in sys.path:
sys.path.insert(0, str(PROJECT_ROOT))
from src.experiments.classical import run_grouped_tabular_benchmark, run_zero_shot_tabular
from src.experiments.deep import run_grouped_sequence_benchmark, run_zero_shot_sequence
from scripts.run_sequence_benchmark import (
MODEL_FAMILIES,
_atomic_write_csv,
_model_is_complete,
_read_csv_or_empty,
)
from scripts.run_zero_shot_benchmark import _write_target_results, _zero_shot_model_is_complete
def _load_sequence(root: Path, dataset: str) -> tuple[np.ndarray, pd.DataFrame]:
folder = root / "artifacts" / "v3" / "features" / dataset
return np.load(folder / "sequences.npz")["X"], pd.read_csv(folder / "sequence_index.csv")
def _ensure_grouped_results(
root: Path,
dataset: str,
seeds: tuple[int, ...],
max_epochs: int,
patience: int,
batch_size: int,
) -> None:
results = root / "artifacts" / "v3" / "results"
features = root / "artifacts" / "v3" / "features" / dataset
classical_metrics = results / f"{dataset}_classical_fold_metrics.csv"
classical_predictions = results / f"{dataset}_classical_predictions.csv"
if not classical_metrics.exists() or not classical_predictions.exists():
frame = pd.read_csv(features / "features.csv")
metrics, predictions = run_grouped_tabular_benchmark(
frame,
dataset_name=dataset.title(),
n_splits=min(5, frame["battery_id"].nunique()),
seeds=seeds,
)
metrics.to_csv(classical_metrics, index=False)
predictions.to_csv(classical_predictions, index=False)
X, index = _load_sequence(root, dataset)
for family, model_ids in MODEL_FAMILIES.items():
metric_path = results / f"{dataset}_{family}_fold_metrics.csv"
prediction_path = results / f"{dataset}_{family}_predictions.csv"
metrics = _read_csv_or_empty(metric_path)
predictions = _read_csv_or_empty(prediction_path)
n_splits = min(5, index["battery_id"].nunique())
for model_id in model_ids:
if _model_is_complete(metrics, predictions, model_id, seeds, n_splits):
continue
model_metrics, model_predictions = run_grouped_sequence_benchmark(
X,
index,
dataset_name=dataset.title(),
n_splits=n_splits,
seeds=seeds,
max_epochs=max_epochs,
patience=patience,
batch_size=batch_size,
model_ids=(model_id,),
)
if not metrics.empty and "model" in metrics:
metrics = metrics[metrics["model"] != model_id]
if not predictions.empty and "model" in predictions:
predictions = predictions[predictions["model"] != model_id]
metrics = pd.concat([metrics, model_metrics], ignore_index=True)
predictions = pd.concat([predictions, model_predictions], ignore_index=True)
_atomic_write_csv(metrics, metric_path)
_atomic_write_csv(predictions, prediction_path)
def _ensure_zero_shot_results(
root: Path,
dataset: str,
seeds: tuple[int, ...],
max_epochs: int,
patience: int,
batch_size: int,
) -> None:
results = root / "artifacts" / "v3" / "results"
feature_root = root / "artifacts" / "v3" / "features"
source = pd.read_csv(feature_root / "nasa" / "features.csv")
target = pd.read_csv(feature_root / dataset / "features.csv")
tabular_metrics = results / f"nasa_to_{dataset}_classical_metrics.csv"
tabular_predictions = results / f"nasa_to_{dataset}_classical_predictions.csv"
if not tabular_metrics.exists() or not tabular_predictions.exists():
metrics, predictions = run_zero_shot_tabular(
source,
target,
target_name=dataset.title(),
random_state=42,
)
metrics.to_csv(tabular_metrics, index=False)
predictions.to_csv(tabular_predictions, index=False)
source_X, source_index = _load_sequence(root, "nasa")
targets = {
target_name.title(): _load_sequence(root, target_name)
for target_name in ("calce", "oxford")
}
for family, model_ids in MODEL_FAMILIES.items():
for model_id in model_ids:
if _zero_shot_model_is_complete(results, family, model_id, seeds):
continue
metrics, predictions = run_zero_shot_sequence(
source_X,
source_index,
targets,
seeds=seeds,
max_epochs=max_epochs,
patience=patience,
batch_size=batch_size,
model_ids=(model_id,),
)
_write_target_results(results, family, metrics, predictions, merge=True)
def run_external_validation(
project_root: str | Path,
*,
dataset: str,
seeds: tuple[int, ...] = (17, 42, 2026),
max_epochs: int = 200,
patience: int = 20,
batch_size: int = 64,
) -> pd.DataFrame:
dataset = dataset.lower()
if dataset not in {"calce", "oxford"}:
raise ValueError("dataset must be 'calce' or 'oxford'")
root = Path(project_root)
result_dir = root / "artifacts" / "v3" / "results"
result_dir.mkdir(parents=True, exist_ok=True)
_ensure_grouped_results(root, dataset, seeds, max_epochs, patience, batch_size)
_ensure_zero_shot_results(root, dataset, seeds, max_epochs, patience, batch_size)
frames = []
for path in sorted(result_dir.glob(f"{dataset}_*_fold_metrics.csv")):
frame = pd.read_csv(path)
frame["validation"] = "within_dataset_grouped"
frames.append(frame)
for path in sorted(result_dir.glob(f"nasa_to_{dataset}_*_metrics.csv")):
frame = pd.read_csv(path)
frame["validation"] = "nasa_zero_shot"
frames.append(frame)
combined = pd.concat(frames, ignore_index=True)
summary = (
combined.groupby(["validation", "model"], as_index=False)
.agg(
mae=("mae", "mean"),
rmse=("rmse", "mean"),
r2=("r2", "mean"),
adjusted_r2=("adjusted_r2", "mean"),
mape=("mape", "mean"),
within_5pp=("within_5pp", "mean"),
)
.sort_values(["validation", "mae"])
)
return summary
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("dataset", choices=("calce", "oxford"))
parser.add_argument("--project-root", type=Path, default=PROJECT_ROOT)
parser.add_argument("--max-epochs", type=int, default=200)
parser.add_argument("--patience", type=int, default=20)
parser.add_argument("--batch-size", type=int, default=64)
parser.add_argument("--seeds", type=int, nargs="+", default=(17, 42, 2026))
args = parser.parse_args()
summary = run_external_validation(
args.project_root,
dataset=args.dataset,
seeds=tuple(args.seeds),
max_epochs=args.max_epochs,
patience=args.patience,
batch_size=args.batch_size,
)
output = args.project_root / "artifacts" / "v3" / "results" / f"{args.dataset}_validation_summary.csv"
summary.to_csv(output, index=False)
print(summary.to_string(index=False))
if __name__ == "__main__":
main()
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