GitLab

* updated gitignore

* updated tree index with cat feature

* added get_split_value_histogram method

* added param for ordinary return value

* fixed number of tests in pytest

* fixed data shape and removed unused code

* refactored tests

* hotfix

* hotfix

* Initial attempt to implement appending features in-memory to another data set

The intent is for this to enable munging files together easily, without needing to round-trip via numpy or write multiple copies to disk.
In turn, that enables working more efficiently with data sets that were written separately.

* Implement Dataset.dump_text, and fix small bug in appending of group bin boundaries.

Dumping to text enables us to compare results, without having to worry about issues like features being reordered.

* Add basic tests for validation logic for add_features_from.

* Remove various internal mapping items from dataset text dumps

These are too sensitive to the exact feature order chosen, which is not visible to the user.
Including them in tests appears unnecessary, as the data dumping code should provide enough coverage.

* Add test that add_features_from results in identical data sets according to dump_text.

* Add test that booster behaviour after using add_features_from matches that of training on the full data

This checks:
- That training after add_features_from works at all
- That add_features_from does not cause training to misbehave

* Expose feature_penalty and monotone_types/constraints via get_field

These getters allow us to check that add_features_from does the right thing with these vectors.

* Add tests that add_features correctly handles feature_penalty and monotone_constraints.

* Ensure add_features_from properly frees the added dataset and add unit test for this

Since add_features_from moves the feature group pointers from the added dataset to the dataset being added to, the added dataset is invalid after the call.
We must ensure we do not try and access this handle.

* Remove some obsolete TODOs

* Tidy up DumpTextFile by using a single iterator for each feature

This iterators were also passed around as raw pointers without being freed, which is now fixed.

* Factor out offsetting logic in AddFeaturesFrom

* Remove obsolete TODO

* Remove another TODO

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.

* Add documentation for newly-added methods.

* Fix whitespace issues identified by pylint.

* Fix a few more whitespace issues.

* Fix doc comments

* Implement deep copying for feature groups.

* Replace awkward std::move usage by emplace_back, and reduce vector size to num_features rather than num_total_features.

* Copy feature groups in addFeaturesFrom, rather than moving them.

* Fix bugs in FeatureGroup copy constructor and ensure source dataset remains usable

* Add reserve to PushVector and PushOffset

* Move definition of Clone into class body

* Fix PR review issues

* Fix for loop increment style.

* Fix test failure

* Some more docstring fixes.

* Remove blank line

[docs] added notes about params usage when data is provided via path and removed unused param (#2024)

* added notes about params usage when data is provided via path

* fixed init score and valid init score params note

* fixed binary params description

* added LightGBM SWIG wrappers for macOS and updated docs

* updated installation instructions based on comments

* updated based on comments

* it is confusing to name validation data `test_data` especially as terms like train, validation, test splits are common in ML. Change variable name in python quick start.

* Update VERSION.txt

* Update .appveyor.yml

* Update DESCRIPTION

* Update DESCRIPTION

* Update DESCRIPTION

* Hand edit broken commit

* Hand edit broken commit

* Hand edit broken commit

* Hand edit broken commit

* convert datatable to numpy directly

* fix according to comments

* updated more docstrings

* simplified isinstance check

* Update compat.py

[docs] Fixed OpenCL Debian package name typo

* refined command status check

* refined Appveyor

* redirect all warnings to stdout

* added OpenMP options for python-package installation

* fixed grammar typo

* always save the score of the first round in early stopping

fix #1971

* avoid using std::log on non-positive numbers

* remove unnecessary changes

* add tests

* Update test_sklearn.py

* enhanced tests

* added test for huge string model

* fixed tree sizes field

* simplified model structure

* fixed test and added try/except

[tests][python] added tests for metrics' behavior and fixed case for multiclass task with custom objective (#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

* Fix build on macOS Mojave

Fixed #1898

- https://iscinumpy.gitlab.io/post/omp-on-high-sierra/
- https://cliutils.gitlab.io/modern-cmake/chapters/packages/OpenMP.html
- https://github.com/Homebrew/homebrew-core/pull/20589

* update setup.py

* update docs

* fix setup.py

* update docs

* update docs

* update setup.py

* update docs

* 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

* fixed minor typos in documentation

* fixed typo in gpu_tree_learner.cpp

* Update .gitignore

* Added links to the libraries used.

* Fixing the header

* Fixes

* ot -> to

* Correcting lgb.prepare output comment

* updated Roxygen files

* Fix multiclass custom objective demo

* Use option not to boost from average instead of setting init score explicitly

* Reference #1846 when turning off boost_from_average

* Add trailing whitespace