[ENH] Venn diagram on sparse data #2334
Merged
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
Codecov Report
@@ Coverage Diff @@
## master #2334 +/- ##
==========================================
- Coverage 73.39% 73.35% -0.04%
==========================================
Files 317 317
Lines 55619 55595 -24
==========================================
- Hits 40819 40782 -37
- Misses 14800 14813 +13 |
|
Added a test for varying_between. |
lanzagar
force-pushed
the
venn_sparse
branch
2 times, most recently
from
35a07a9
to
329f8c4
Compare
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Outdated suggestions cannot be applied.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.
Issue
Fixes #2164.
Description of changes
Venn diagram is fixed to work on sparse data.
The methods
reshape_wide()andvarying_between()have been rewritten towork on sparse data, by iterating over non-zero values rather than all values.
Remaining issues:
Table.transform(domain)in thereshape_wide()method, which iterates over all columns.Current timings:
~10 sec on 200 x 10000 dataset.
~2 min on 1200 x 24000 dataset.
too long on 61000 x 24000 dataset (friends-transcripts ; takes 1.2 GB memory).
Includes