[Code scan] Constant stacking-fault predictions can produce invalid derivative metrics

[njzjz]

This issue was found by a Codex global repository scan of tracked non-test files at commit 8c93925cb10b401b2b83c738bd9263fd74474468.

Relevant code

LAMBench/lambench/tasks/calculator/stacking_fault/stacking_fault.py

Lines 34 to 49 in 8c93925

	_, y_smooth_pred = fit_pchip(
	res, x_col="Displacement", y_col="Energy", num_points=NUM_POINTS
	)
	derivative_label = (
	(y_smooth_label[1:] - y_smooth_label[:-1])
	* (NUM_POINTS - 1)
	/ max(y_smooth_label)
	)
	derivative_pred = (
	(y_smooth_pred[1:] - y_smooth_pred[:-1]) * (NUM_POINTS - 1) / max(y_smooth_pred)
	)
	return np.round(mean_absolute_error(y_smooth_label, y_smooth_pred), 4), np.round(
	mean_absolute_error(derivative_label, derivative_pred), 4
	)

Impact

The derivative metric normalizes by max(y_smooth_pred). A calculator that predicts a flat energy curve produces y_smooth_pred == 0 after subtracting the minimum, so the denominator is zero. The resulting NaN values can make the derivative MAE invalid or fail the task instead of assigning a defined penalty for a bad trajectory.

Suggested fix

Guard zero or near-zero denominators and assign a defined derivative penalty, or mark that trajectory as failed while allowing the rest of the task to complete. Add a regression test with constant predicted energies.