[ENH][DOC] local join count and LOSH statistics #139
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@@ Coverage Diff @@
## master #139 +/- ##
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+ Coverage 77.61% 79.49% +1.88%
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Files 25 33 +8
Lines 3274 3584 +310
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+ Hits 2541 2849 +308
- Misses 733 735 +2
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ljwolf
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Aug 28, 2020
ljwolf
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Aug 28, 2020
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👏 this is amazing @jeffcsauer! well done! extremely minor API changes that mesh with the sklearn developer guide, but jeez, these are awesome!
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This has been superseded by #145 |
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Purpose of PR
This PR addresses bullet point 1, 5, and 6 of #61. Specifically, this PR adds four new functions to estimate:
losh)Local_Join_Count)Local_Join_Count_BV)Local_Join_Count_MV)Each function is written in the form of a scikit-learn style estimator. PEP8 formatting has been applied to all of the functions, although a handful of lines are left long due to readability.
Documentation
Two notebooks are included in the PR. Each notebook briefly reviews the core math of the statistic and explains how a user might deploy the statistic in a geospatial workflow. The included examples match those found in
Rspdep::LOSHandspdep::LOSH.csas well as the GeoDa Local Join Counts tutorial.All functions include docstrings and doctests that use built-in PySAL example datasets.
Tests
I have added in four .py test files that execute successfully on my fork. These new tests follow the lead of the existing tests, specifically
test_moran.pyfor the LOSH function andtest_join_counts.pyfor the local join count functions.Notes for PR consideration
n_jobs=1to avoid issues fixed in potential merges. Users may enter values other than 1.commpop, appears to have a dead link at the moment. Having trouble loading this as alibpysal.example. Once this dataset is working the doctest can be updated. Currently hosting it on a personal Github link.