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4671c68 | 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 | #!/usr/bin/env python3
"""
Example: train a BatteryMHM state-of-health predictor on your own cells.
Replace `load_my_cells()` with your real data: for each cell you need an
early-cycle capacity curve (a 1-D array of per-cycle discharge capacities)
and a target SOH label (e.g. capacity retention at end of life).
Run: python examples/predict_soh.py
"""
import numpy as np
from batterymhm import MHMEnsemble, compute_metrics, mhm_full_features, seq_to_harmonics
def load_my_cells():
"""
Stand-in data loader — returns (curves, soh_labels).
Swap this out for your measured cells (e.g. parsed from a cycler export).
"""
rng = np.random.default_rng(7)
curves, labels = [], []
for _ in range(120):
rate = rng.uniform(0.001, 0.004)
cap = 1.0 - rate * np.sqrt(np.arange(1, 61)) + rng.normal(0, 0.0015, 60)
curves.append(np.clip(cap, 0, 1.05))
labels.append(float(cap[-1]))
return curves, np.asarray(labels)
def featurize(curves):
"""Quantise each curve to HINs and build the MHM descriptor matrix."""
dicts = [mhm_full_features(seq_to_harmonics(list(c), bins=9)) for c in curves]
keys = sorted(dicts[0]) # stable column order
X = np.array([[d[k] for k in keys] for d in dicts], dtype=float)
return X, keys
def main():
curves, y = load_my_cells()
X, keys = featurize(curves)
n = len(y)
cut = int(0.75 * n)
model = MHMEnsemble().fit(X[:cut], y[:cut], feature_names=keys)
pred = model.predict(X[cut:])
print("Held-out performance:", compute_metrics(y[cut:], pred))
print("\nMost informative MHM features:")
for name, imp in model.top_features(8):
print(f" {imp:6.4f} {name}")
if __name__ == "__main__":
main()
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