Closed martinfleis closed 1 year ago
mmm. New failures are caused by fresh scikit-learn 1.3.0 where KDTree.valid_metrics
is no longer an attribute but a method. Will look into that.
Apparently KDtree.valid_metrics
was not considered public before? And they now added KDtree.valid_metrics()
in https://github.com/scikit-learn/scikit-learn/pull/25482 See https://github.com/scikit-learn/scikit-learn/issues/26742
Merging #107 (8d616ca) into main (44ce06b) will increase coverage by
0.18%
. The diff coverage is100.00%
.
@@ Coverage Diff @@
## main #107 +/- ##
==========================================
+ Coverage 52.38% 52.56% +0.18%
==========================================
Files 12 12
Lines 1844 1851 +7
Branches 316 317 +1
==========================================
+ Hits 966 973 +7
Misses 821 821
Partials 57 57
Impacted Files | Coverage Δ | |
---|---|---|
pointpats/geometry.py | 78.57% <100.00%> (+0.93%) |
:arrow_up: |
pointpats/pointpattern.py | 70.05% <100.00%> (ø) |
sorry for a self-merge but this was completely blocking any testing now and I wanted to work on #109 and #108
Note to myself that we may need to eventually revert the sklearn compat part because it is a regression in upstream and they will restore the original behaviour (property, not a method).
It seems that the DataFrame input to query worked but was never actually supported. This ensures we always cast it to a numpy array.
Closes #106
We should probably cut a patch release with this as any env with new scipy will have this issue.