Add weighted_n_node_samples field in sklearn importer #330
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Codecov Report
@@ Coverage Diff @@
## mainline #330 +/- ##
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- Coverage 85.06% 84.16% -0.90%
Complexity 42 42
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Files 108 108
Lines 8374 8350 -24
Branches 40 40
==============================================
- Hits 7123 7028 -95
- Misses 1228 1299 +71
Partials 23 23
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This was referenced Jan 13, 2022
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Jan 25, 2022
The 2.2.0 version of Treelite incorporates the following major improvements: * dmlc/treelite#314 * dmlc/treelite#322, dmlc/treelite#327 * dmlc/treelite#325 * dmlc/treelite#332 * dmlc/treelite#330 * dmlc/treelite#333 * dmlc/treelite#334 * dmlc/treelite#304 * dmlc/treelite#335 In particular, dmlc/treelite#332, dmlc/treelite#330, dmlc/treelite#333 are required for #4447. Requires rapidsai/integration#412. EDIT. Using 2.2.1 patch release, to incorporate a hotfix (dmlc/treelite#340). Authors: - Philip Hyunsu Cho (https://github.com/hcho3) Approvers: - AJ Schmidt (https://github.com/ajschmidt8) - Dante Gama Dessavre (https://github.com/dantegd) URL: #4484
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Add support for: - [x] cuML RF classifiers - [x] scikit-learn RF regressors - [x] scikit-learn RF classifiers TODOs - [x] Add test cases - [x] De-duplicate path extraction logic Requires dmlc/treelite#330 Authors: - Philip Hyunsu Cho (https://github.com/hcho3) Approvers: - William Hicks (https://github.com/wphicks) - Dante Gama Dessavre (https://github.com/dantegd) URL: #4447
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The 2.2.0 version of Treelite incorporates the following major improvements: * dmlc/treelite#314 * dmlc/treelite#322, dmlc/treelite#327 * dmlc/treelite#325 * dmlc/treelite#332 * dmlc/treelite#330 * dmlc/treelite#333 * dmlc/treelite#334 * dmlc/treelite#304 * dmlc/treelite#335 In particular, dmlc/treelite#332, dmlc/treelite#330, dmlc/treelite#333 are required for rapidsai#4447. Requires rapidsai/integration#412. EDIT. Using 2.2.1 patch release, to incorporate a hotfix (dmlc/treelite#340). Authors: - Philip Hyunsu Cho (https://github.com/hcho3) Approvers: - AJ Schmidt (https://github.com/ajschmidt8) - Dante Gama Dessavre (https://github.com/dantegd) URL: rapidsai#4484
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…#4447) Add support for: - [x] cuML RF classifiers - [x] scikit-learn RF regressors - [x] scikit-learn RF classifiers TODOs - [x] Add test cases - [x] De-duplicate path extraction logic Requires dmlc/treelite#330 Authors: - Philip Hyunsu Cho (https://github.com/hcho3) Approvers: - William Hicks (https://github.com/wphicks) - Dante Gama Dessavre (https://github.com/dantegd) URL: rapidsai#4447
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Scikit-learn tree models has two fields for sample counts in nodes:
n_node_samples(unweighted count, int64) andweighted_n_node_samples(weighted count, float64). So far, Treelite kept only the unweighted count and discarded the weighted count.This PR stores
weighted_n_node_samplesin the Treelite object, using thesum_hessfield.Required by rapidsai/cuml#4447