Closed michaelbornholdt closed 3 years ago
Merging #52 (5fb4d24) into master (220b296) will decrease coverage by
0.09%
. The diff coverage is97.29%
.:exclamation: Current head 5fb4d24 differs from pull request most recent head 04be210. Consider uploading reports for the commit 04be210 to get more accurate results
@@ Coverage Diff @@
## master #52 +/- ##
==========================================
- Coverage 98.36% 98.26% -0.10%
==========================================
Files 24 24
Lines 855 865 +10
==========================================
+ Hits 841 850 +9
- Misses 14 15 +1
Flag | Coverage Δ | |
---|---|---|
unittests | 98.26% <97.29%> (-0.10%) |
:arrow_down: |
Flags with carried forward coverage won't be shown. Click here to find out more.
Impacted Files | Coverage Δ | |
---|---|---|
cytominer_eval/evaluate.py | 100.00% <ø> (ø) |
|
cytominer_eval/tests/test_evaluate.py | 100.00% <ø> (ø) |
|
cytominer_eval/operations/enrichment.py | 95.45% <93.33%> (-4.55%) |
:arrow_down: |
cytominer_eval/operations/precision_recall.py | 100.00% <100.00%> (ø) |
|
...iner_eval/tests/test_operations/test_enrichment.py | 100.00% <100.00%> (ø) |
|
...val/tests/test_operations/test_precision_recall.py | 100.00% <100.00%> (ø) |
|
cytominer_eval/transform/util.py | 100.00% <100.00%> (ø) |
Continue to review full report at Codecov.
Legend - Click here to learn more
Δ = absolute <relative> (impact)
,ø = not affected
,? = missing data
Powered by Codecov. Last update 220b296...04be210. Read the comment docs.
@gwaygenomics This last Commit should fix every thing. So this is ready for review :)
Fixes #51
Ah, I still need to run the Demo notebook
@gwaygenomics I reran the demo, All up to do date now
@gwaygenomics should be done with all your comments now
@gwaygenomics ready for you to look at again :)
@gwaygenomics back to you again :)
What is this change request, how do I get rid of it?
No worries, I get rid of it when I finally approve. Looking now
Wonderfully done - merging now
Enrichment and Precision recall shall intake a list of variables. This way the similarity matrix is only computed once!