History

[POC] Experimental support for l1 error. (#7812 )

Support adaptive tree, a feature supported by both sklearn and lightgbm.  The tree leaf is recomputed based on residue of labels and predictions after construction.

For l1 error, the optimal value is the median (50 percentile).

This is marked as experimental support for the following reasons:
- The value is not well defined for distributed training, where we might have empty leaves for local workers. Right now I just use the original leaf value for computing the average with other workers, which might cause significant errors.
- Some follow-ups are required, for exact, pruner, and optimization for quantile function. Also, we need to calculate the initial estimation.

2022-04-26 21:41:55 +08:00

benchmark

Fix spelling in documents (#6948 )

2021-05-11 20:44:36 +08:00

ci_build

Drop support for deprecated CUDA architectures. (#7774 )

2022-03-31 21:42:23 +08:00

cli

Fix data loading (#4862 )

2019-10-22 12:33:14 -04:00

cpp

[POC] Experimental support for l1 error. (#7812 )

2022-04-26 21:41:55 +08:00

distributed

Fix MPI build. (#6403 )

2020-11-21 13:38:21 +08:00

python

[POC] Experimental support for l1 error. (#7812 )

2022-04-26 21:41:55 +08:00

python-gpu

[POC] Experimental support for l1 error. (#7812 )

2022-04-26 21:41:55 +08:00

travis

Move macos test to github action. (#7382 )

2021-10-30 14:40:32 +08:00

jenkins_get_approval.py

[CI] Enforce daily budget in Jenkins CI (#5884 )

2020-07-13 21:51:11 -07:00

pytest.ini

RMM integration plugin (#5873 )

2020-08-12 01:26:02 -07:00

README.md

Fix spelling in documents (#6948 )

2021-05-11 20:44:36 +08:00

README.md

This folder contains test cases for XGBoost c++ core, Python package and some other CI facilities.

Directories

ci_build: Test facilities for Jenkins CI and GitHub action.
cli: Basic test for command line executable xgboost. Most of the other command line specific tests are in Python test test_cli.py
cpp: Tests for C++ core, using Google test framework.
python: Tests for Python package, demonstrations and CLI. For how to setup the dependencies for tests, see conda files in ci_build.
python-gpu: Similar to python tests, but for GPU.
travis: CI facilities for Travis.
distributed: Legacy tests for distributed system. Most of the distributed tests are in Python tests using dask and jvm package using spark.
benchmark: Legacy benchmark code. There are a number of benchmark projects for XGBoost with much better configurations.

Others

pytest.ini: Describes the pytest marker for python tests, some markers are generated by conftest.py file.