* Implement early stopping with training continuation.
* Add new C API for obtaining boosted rounds.
* Fix off by 1 in `save_best`.
Co-authored-by: Philip Hyunsu Cho <chohyu01@cs.washington.edu>
* Do not derive from unittest.TestCase (not needed for pytest)
* assertRaises -> pytest.raises
* Simplify test_empty_dmatrix with test parametrization
* setUpClass -> setup_class, tearDownClass -> teardown_class
* Don't import unittest; import pytest
* Use plain assert
* Use parametrized tests in more places
* Fix test_gpu_with_sklearn.py
* Put back run_empty_dmatrix_reg / run_empty_dmatrix_cls
* Fix test_eta_decay_gpu_hist
* Add parametrized tests for monotone constraints
* Fix test names
* Remove test parametrization
* Revise test_slice to be not flaky
Deprecate positional arguments in following functions:
- `__init__` for all classes in sklearn module.
- `fit` method for all classes in sklearn module.
- dask interface.
- `set_info` for `DMatrix` class.
Refactor the evaluation matrices handling.
* Deprecate LabelEncoder in XGBClassifier; skip LabelEncoder for cuDF/cuPy inputs
* Add unit tests for cuDF and cuPy inputs with XGBClassifier
* Fix lint
* Clarify warning
* Move use_label_encoder option to XGBClassifier constructor
* Add a test for cudf.Series
* Add use_label_encoder to XGBRFClassifier doc
* Address reviewer feedback
* Publish artifacts only on the master and release branches
* Build CUDA only for Compute Capability 7.5 when building PRs
* Run all Windows jobs in a single worker image
* Build nightly XGBoost4J SNAPSHOT JARs with Scala 2.12 only
* Show skipped Python tests on Windows
* Make Graphviz optional for Python tests
* Add back C++ tests
* Unstash xgboost_cpp_tests
* Fix label to CUDA 10.1
* Install cuPy for CUDA 10.1
* Install jsonschema
* Address reviewer's feedback
* Simplify Scikit-Learn parameter management.
* Copy base class for removing duplicated parameter signatures.
* Set all parameters to None.
* Handle None in set_param.
* Extract the doc.
Co-authored-by: Jiaming Yuan <jm.yuan@outlook.com>
* Fix syncing DMatrix columns.
* notes for tree method.
* Enable feature validation for all interfaces except for jvm.
* Better tests for boosting from predictions.
* Disable validation on JVM.
* _maybe_pandas_xxx should return their arguments unchanged if no pandas installed
* Tests should not assume pandas is installed
* Mark tests which require pandas as such
* Implement tree model dump with a code generator.
* Split up generators.
* Implement graphviz generator.
* Use pattern matching.
* [Breaking] Return a Source in `to_graphviz` instead of Digraph in Python package.
Co-Authored-By: Philip Hyunsu Cho <chohyu01@cs.washington.edu>
* Brought the silent parameter for the SKLearn-like API back, marked it deprecated.
- added deprecation notice and warning
- removed silent from the tests for the SKLearn-like API
* Added SKLearn-like random forest Python API.
- added XGBRFClassifier and XGBRFRegressor classes to SKL-like xgboost API
- also added n_gpus and gpu_id parameters to SKL classes
- added documentation describing how to use xgboost for random forests,
as well as existing caveats
* Enable xgb_model parameter in XGClassifier scikit-learn API
https://github.com/dmlc/xgboost/issues/3049
* add test_XGBClassifier_resume():
test for xgb_model parameter in XGBClassifier API.
* Update test_with_sklearn.py
* Fix lint
* use gain for sklearn feature_importances_
`gain` is a better feature importance criteria than the currently used `weight`
* added importance_type to class
* fixed test
* white space
* fix variable name
* fix deprecation warning
* fix exp array
* white spaces
* Fix#3730: scikit-learn 0.20 compatibility fix
sklearn.cross_validation has been removed from scikit-learn 0.20,
so replace it with sklearn.model_selection
* Display test names for Python tests for clarity
* Fix#3648: XGBClassifier.predict() should return margin scores when output_margin=True
* Fix tests to reflect correct implementation of XGBClassifier.predict(output_margin=True)
* Fix flaky test test_with_sklearn.test_sklearn_api_gblinear
* added xgbranker
* fixed predict method and ranking test
* reformatted code in accordance with pep8
* fixed lint error
* fixed docstring and added checks on objective
* added ranking demo for python
* fixed suffix in rank.py
* Add option to use weights when evaluating metrics in validation sets
* Add test for validation-set weights functionality
* simplify case with no weights for test sets
* fix lint issues
* Added kwargs support for Sklearn API
* Updated NEWS and CONTRIBUTORS
* Fixed CONTRIBUTORS.md
* Added clarification of **kwargs and test for proper usage
* Fixed lint error
* Fixed more lint errors and clf assigned but never used
* Fixed more lint errors
* Fixed more lint errors
* Fixed issue with changes from different branch bleeding over
* Fixed issue with changes from other branch bleeding over
* Added note that kwargs may not be compatible with Sklearn
* Fixed linting on kwargs note
* Added n_jobs and random_state to keep up to date with sklearn API.
Deprecated nthread and seed. Added tests for new params and
deprecations.
* Fixed docstring to reflect updates to n_jobs and random_state.
* Fixed whitespace issues and removed nose import.
* Added deprecation note for nthread and seed in docstring.
* Attempted fix of deprecation tests.
* Second attempted fix to tests.
* Set n_jobs to 1.
* Add option to choose booster in scikit intreface (gbtree by default)
* Add option to choose booster in scikit intreface: complete docstring.
* Fix XGBClassifier to work with booster option
* Added test case for gblinear booster
