Restore prior efficiency by making matching a ton faster (also make -x filtering faster) #174
Conversation
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This should represent a pretty decent performance boost (#172)
Turns out this is a ton more efficient. Added bonus of now relying on pandas' implementation of this instead of ours. Turns out transposing huge dataframes is a pretty significant endeavor, so calling .T on the feature table for like the EMP dataset was taking a super long time. Fortunately, we can finesse our way around this by instead transposing the sample metadata and then aligning on the columns. I'm glad that we reached a solution for this that preserved all of the matching-up-front niceness re: testing. Solid stuff. Uh, next up are #171 and then #58? But we can def merge this back into master now.
Codecov Report
@@ Coverage Diff @@
## master #174 +/- ##
==========================================
+ Coverage 91.92% 91.95% +0.03%
==========================================
Files 15 15
Lines 1040 1044 +4
Branches 180 180
==========================================
+ Hits 956 960 +4
Misses 69 69
Partials 15 15
Continue to review full report at Codecov.
|
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
tl;dr—taking the transpose of a huge feature table in pandas is super intensive, as is calling
.loc[]on said huge feature table.Fortunately, there's the
DataFrame.align()method, which lets us delegate most of this work to pandas. This is much, much faster thanmatchdf().(And we get around the transpose issue now by temporarily transposing the sample metadata instead of the feature table—generally, the sample metadata should be a lot smaller. I think this might be a pandas bug, actually? see pandas-dev/pandas#22630 -- will try to report on something there)
In any case, it's now pretty fast again to run the EMP dataset (with poisson-cat differentials) through Qurro (assuming -x 2000 is passed, as before).