Functional #189
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Functional #189
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Codecov Report
@@ Coverage Diff @@
## master #189 +/- ##
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- Coverage 80.59% 77.88% -2.72%
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Files 42 45 +3
Lines 4277 4444 +167
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+ Hits 3447 3461 +14
- Misses 830 983 +153
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|
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" |
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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.
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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.