[Code scan] NEB zero failure rates are emitted as missing 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/neb/neb.py

Lines 62 to 86 in 8c93925

	result_df["type"] = result_df["traj"].apply(lambda x: x.split("_")[0])
	result_df["error"] = result_df.apply(
	lambda x: np.abs(x["pred_Ea"] - x["Ea"]), axis=1
	)
	type_percentages = (
	result_df[result_df["error"] > ERROR_THRESHOLD].groupby("type").size()
	/ result_df.groupby("type").size()
	* 100
	)
	type_percentages = type_percentages.round(2)
	# update key names
	type_percentages.rename(
	index={
	"desorption": "ø_Desorption",
	"dissociation": "ø_Dissociation",
	"transfer": "ø_Transfer",
	},
	inplace=True,
	)
	results = type_percentages.to_dict()
	result_df.dropna(inplace=True)
	results["success_rate"] = len(result_df) / NUM_RECORDS * 100
	results["MAE_Ea"] = mean_absolute_error(result_df["Ea"], result_df["pred_Ea"])
	results["MAE_dE"] = mean_absolute_error(result_df["dE"], result_df["pred_dE"])

Impact

The NEB failure percentage is computed from only the rows where error > ERROR_THRESHOLD. If a reaction type has zero failures, that type is absent from the numerator groupby, so the division can produce a missing value instead of an explicit 0.0 failure rate.

A perfect category should be represented as 0.0, not as a missing metric. Otherwise downstream JSON and leaderboard plots can treat a valid result as absent.

Suggested fix

Build the denominator counts first, reindex the failure counts against those types with fill_value=0, then divide. Add a regression case where one NEB type has no failures.