Optimize HGMD information search in Feature Engineering Part 1 #61
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hyunhwan-bcm
approved these changes
Aug 19, 2024
| """ | ||
| function to get HGMD from local flat file | ||
| Params: | ||
| varObj:a varaint object read from VEP annotation | ||
| hgmdDf: HGMD data frame read from local file (CL: now it refers to hgmdHPOScoreDf in main.py) |
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thanks for clean the comments up
| # print('\thgmdVarFound:',hgmdVarFound,'hgmdGeneFound:',hgmdGeneFound, | ||
| # 'hgmdVarPhenIdList:',hgmdVarPhenIdList,'hgmdVarHPOIdList:',hgmdVarHPOIdList, | ||
| # 'hgmdVarHPOStrList:',hgmdVarHPOStrList) | ||
| # return | ||
| retList = [ |
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not sure we need other things here, but let's keep them then remove in future PR.
hyunhwan-bcm
approved these changes
Aug 19, 2024
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Background
In Issue #54, we identified that Feature Engineering Part 1 was the primary bottleneck when processing WGS-level VCF files.
Based on profiling by @hyunhwan-bcm, we pinpointed the following lines as the main culprits for the slowdown:
Given that these operations are executed 400,000 times (with
Nas the number of rows invarDf) andhgmdDfhas 350,000 rows (denoted byM), the time complexity scales toO(NM). This results in an intractable number of operations, proportional to 140 billion pairs.Optimized Code
We've improved the implementation to achieve
O(N log M)complexity:Where,
Conclusion
After applying these optimizations, the processing time for WGS data was reduced from 5 hours to 42 minutes, with identical output to the original implementation.