Functional #189
Open
Functional #189
+158
−57
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 ReportAttention: Patch coverage is
Additional details and impacted files@@ Coverage Diff @@
## main #189 +/- ##
=======================================
- Coverage 80.6% 79.6% -1.0%
=======================================
Files 42 45 +3
Lines 4277 4644 +367
=======================================
+ Hits 3447 3695 +248
- Misses 830 949 +119
|
ljwolf
commented
Sep 9, 2021
| klassname = klass.__name__ | ||
| name = klass.__name__.lower() | ||
| if klassname == "LOSH": | ||
| defn = f"def {name}(*args, **kwargs):\n\tobj = {klassname}(*args, **kwargs)\n\treturn obj._statistic, obj.pval" |
There was a problem hiding this comment.
These have to separate the init and fit phases, so I need to figure out how to re-write the signature so that the options all go through.
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Outdated suggestions cannot be applied.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.
This is a bit of metaprogramming that re-rexpresses most of the esda stats classes as functions that just return the statistic and a simulated p-value.
Still needs work with the LOSH and Spatial_Pearson statistics, but this is one of the two APIs I think we should adopt. The other is the LOSH/Spatial_Pearson-style sklearn estimator.