codecov[bot]
commented
3 years ago Codecov Report
Merging #23 (6e698c0) into master (0a42d68) will increase coverage by
0.22%. The diff coverage is100.00%.
@@ Coverage Diff @@
## master #23 +/- ##
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+ Coverage 99.77% 100.00% +0.22%
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Files 11 12 +1
Lines 440 559 +119
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+ Hits 439 559 +120
+ Misses 1 0 -1 | Impacted Files | Coverage Δ | |
|---|---|---|
| sklearn_pmml_model/base.py | 100.00% <100.00%> (ø) |
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| sklearn_pmml_model/ensemble/__init__.py | 100.00% <100.00%> (ø) |
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| sklearn_pmml_model/ensemble/forest.py | 100.00% <100.00%> (ø) |
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| sklearn_pmml_model/ensemble/gb.py | 100.00% <100.00%> (ø) |
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| sklearn_pmml_model/linear_model/implementations.py | 100.00% <100.00%> (ø) |
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| sklearn_pmml_model/naive_bayes/implementations.py | 100.00% <100.00%> (ø) |
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| sklearn_pmml_model/tree/__init__.py | 100.00% <100.00%> (ø) |
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| sklearn_pmml_model/tree/tree.py | 100.00% <100.00%> (+1.02%) |
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| ... and 2 more |
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lgtm-com[bot]
The long awaited gradient boosting model support is finally here.
I have found that PMML generation for GBM models is unfortunately not very consistent. I had to account for many different versions which resulted in a rather massive change.
There are some parts I am yet uncertain about:
Target.rescaleFactorseems related. I've added a FIXME note, and it should be revisited when supporting regression trees outside of gradient boosting.pypmml. This seems happen at https://github.com/iamDecode/sklearn-pmml-model/blob/27cc103997aa186eb3f42809255eb9658631c1dc/sklearn_pmml_model/tree/tree.py#L224, as switching the - for a + fixes the probabilities for those models. However, that inevitably breaks the predictions of other models, which in my testing is way more significant. I am not sure what causes this discrepancy, but was not able to fix it just yet. However, this is such a slight effect I'm willing to accept it for now. Should reinvestigate later.After this PR, supporting regression should be fairly straightforward.
Closes #20