Investigate whether short circuit if statements speed up task sample processing
维护者通常 3 天内回复
@yugdave0911 已经在做这个了。
开始于 2026年4月13日。
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调研方向
The issue names the four task families but no source files or tests; first locate their sample generation code and identify how the MIMIC3 and MIMIC4 datasets are configured. Compare generation time for the existing condition, procedure, and drug checks against the short circuit checks on both full datasets. Update all four task families only if the measurements show a noticeable improvement, and report the comparison.
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描述
The mortality_prediction, drug_recommendation, length_of_stay_prediction, and readmisison_prediction tasks all exclude visits that do not have "conditions" AND "procedures" AND "drugs". The first three exclude them with the following pattern.
if len(conditions) * len(procedures) * len(drugs) == 0:
continue
However, the readmission_prediction tasks short circuits the generation of the procedures and drugs lists if there are no conditions and short circuits the generation of the drugs list if there are no procedures.
conditions = ...
if len(conditions) == 0:
continue
procedures = ...
if len(procedures) == 0:
continue
drugs = ...
if len(drugs) == 0:
continue
The two approaches are logically equivalent. The question is whether the latter is more performant. It's hard to say what Python does with this under the hood.
I did a quick test on one of the small demo/synthetic datasets (I don't remember which one) and there was not a noticeable difference in sample generation time. However, it's very possible we would notice a difference between the two approaches if we ran sample generation on the full MIMIC3 and/or MIMIC4 datasets.
Run said full dataset test/s and if the performance is noticeably different, update all the tasks to use the short circuit ifs.
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