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Upstream updates + data set merging + cost effective gradient boosting #2
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…oft#1953) * removed comparison warning * fixed spacing
* Fix multiclass custom objective demo * Use option not to boost from average instead of setting init score explicitly * Reference microsoft#1846 when turning off boost_from_average * Add trailing whitespace
* Correcting lgb.prepare output comment * updated Roxygen files
* Added links to the libraries used. * Fixing the header * Fixes * ot -> to
* fixed minor typos in documentation * fixed typo in gpu_tree_learner.cpp * Update .gitignore
* add warnings for override parameters of Dataset * fix pep8 * add feature_penalty * refactor * add R's code * Update basic.py * Update basic.py * fix parameter bug * Update lgb.Dataset.R * fix a bug
* Fix build on macOS Mojave Fixed microsoft#1898 - https://iscinumpy.gitlab.io/post/omp-on-high-sierra/ - https://cliutils.gitlab.io/modern-cmake/chapters/packages/OpenMP.html - Homebrew/homebrew-core#20589 * update setup.py * update docs * fix setup.py * update docs * update docs * update setup.py * update docs
…multiclass task with custom objective (microsoft#1954) * added metrics test for standard interface * simplified code * less trees * less trees * use dummy custom objective and metric * added tests for multiclass metrics aliases * fixed bug in case of custom obj and num_class > 1 * added metric test for sklearn wrapper
* added test for huge string model * fixed tree sizes field * simplified model structure * fixed test and added try/except
* always save the score of the first round in early stopping fix microsoft#1971 * avoid using std::log on non-positive numbers * remove unnecessary changes * add tests * Update test_sklearn.py * enhanced tests
…oft#1975) * added OpenMP options for python-package installation * fixed grammar typo
* refined command status check * refined Appveyor * redirect all warnings to stdout
[docs] Fixed OpenCL Debian package name typo
* convert datatable to numpy directly * fix according to comments * updated more docstrings * simplified isinstance check * Update compat.py
* Update DESCRIPTION * Update DESCRIPTION
* Update VERSION.txt * Update .appveyor.yml * Update DESCRIPTION
This one is debatable, test code can be a bit messy and duplicate-heavy, factoring it out tends to end badly. Leaving this for now, will revisit if adding more tests later on becomes a mess.
This currently only merges the feature groups and updates num_data_. It does not deal with Metadata or non-dense bins yet.
This seems silly, but push_buffers_ aren't populated if the data was loaded from a binary file. This forces us to reconstruct the index,value form of the data in the target bin before merging. Adding this test ensures that code is covered.
This is currently not covered by unit tests.
This catches the majority of obvious errors, e.g. not having the same number of features or having different bin mappings.
Like the original CEGB version, this inherits from SerialTreeLearner. Currently, it changes nothing from the original.
This is heavily based on the serial version, but just adds using the coupled penalties.
…rhead of CEGB, and add sanity checks for the lengths of the penalty vectors.
remcob-gr
changed the title
Upstream updates + WFH round 1
Upstream updates + data set merging + cost effective gradient boosting
Feb 13, 2019
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