[Code scan] NEB zero failure rates are emitted as missing metrics

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Assessment

Difficulty
2/5
Estimated time
1-3 hours
Newbie friendliness
88/100
Issue type
Bug
Clarity
Clearly specified
Activity status
Quiet
Tech stack
python
Domain
data

Research direction

Start in lambench/tasks/calculator/neb/neb.py at lines 62-86 and trace how NEB failure counts and denominators are grouped. Add the regression case described in the issue for a type with no failures, then verify the resulting metric is emitted as 0.0 rather than missing.

Written by the indexing model from the issue text.

Description

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

Relevant code

https://github.com/deepmodeling/LAMBench/blob/8c93925cb10b401b2b83c738bd9263fd74474468/lambench/tasks/calculator/neb/neb.py#L62-L86

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.

Dominant language
Python
Stars
20
Forks
11
Avg merge
30m
Merged PRs (30d)
4

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