* Ensure that configured header (build_config.h) from dmlc-core is picked up by Rabit and XGBoost
* Check which Rabit target is being used
* Use CMake 3.13 in all Jenkins tests
* Upgrade CMake in Travis CI
* Install CMake using Kitware installer
* Remove existing CMake (3.12.4)
The setup.py is rewritten. This new script uses only Python code and provide customized
implementation of setuptools commands. This way users can run most of setuptools commands
just like any other Python libraries.
* Remove setup_pip.py
* Remove soft links.
* Define customized commands.
* Remove shell script.
* Remove makefile script.
* Update the doc for building from source.
* Make pip install xgboost*.tar.gz work by fixing build-python.sh
* Simplify install doc
* Add test
* Install Miniconda for Linux target too
* Build XGBoost only once in sdist
* Try importing xgboost after installation
* Don't set PYTHONPATH env var for sdist test
* Add OpenMP as CMake target
* Require CMake 3.12, to allow linking OpenMP target to objxgboost
* Specify OpenMP compiler flag for CUDA host compiler
* Require CMake 3.16+ if the OS is Mac OSX
* Use AppleClang in Mac tests.
* Update dmlc-core
- Install wget explicitly to match openssl.
- Install CMake explicitly.
- Use newer miniconda link.
- Reenable unittests.
- gcc@9 + xcode@10 for osx due to missing <_stdio.h>. Other versions of gcc should also work. But as homebrew pour gcc@9 after update by default, so I just stick with latest version.
- Disabled one external memory test for OSX. Not sure about the thread implementation in there and fixing external memory is beyond the scope of this PR.
- Use Python3 with conda in jvm package.
This makes GPU Hist robust in distributed environment as some workers might not
be associated with any data in either training or evaluation.
* Disable rabit mock test for now: See #5012 .
* Disable dask-cudf test at prediction for now: See #5003
* Launch dask job for all workers despite they might not have any data.
* Check 0 rows in elementwise evaluation metrics.
Using AUC and AUC-PR still throws an error. See #4663 for a robust fix.
* Add tests for edge cases.
* Add `LaunchKernel` wrapper handling zero sized grid.
* Move some parts of allreducer into a cu file.
* Don't validate feature names when the booster is empty.
* Sync number of columns in DMatrix.
As num_feature is required to be the same across all workers in data split
mode.
* Filtering in dask interface now by default syncs all booster that's not
empty, instead of using rank 0.
* Fix Jenkins' GPU tests.
* Install dask-cuda from source in Jenkins' test.
Now all tests are actually running.
* Restore GPU Hist tree synchronization test.
* Check UUID of running devices.
The check is only performed on CUDA version >= 10.x, as 9.x doesn't have UUID field.
* Fix CMake policy and project variables.
Use xgboost_SOURCE_DIR uniformly, add policy for CMake >= 3.13.
* Fix copying data to CPU
* Fix race condition in cpu predictor.
* Fix duplicated DMatrix construction.
* Don't download extra nccl in CI script.
* All Linux tests are now in Jenkins CI
* Tests are now de-coupled from builds. We can now build XGBoost with one version of CUDA/JDK and test it with another version of CUDA/JDK
* Builds (compilation) are significantly faster because 1) They use C5 instances with faster CPU cores; and 2) build environment setup is cached using Docker containers
* Refactor CMake scripts.
* Remove CMake CUDA wrapper.
* Bump CMake version for CUDA.
* Use CMake to handle Doxygen.
* Split up CMakeList.
* Export install target.
* Use modern CMake.
* Remove build.sh
* Workaround for gpu_hist test.
* Use cmake 3.12.
* Revert machine.conf.
* Move CLI test to gpu.
* Small cleanup.
* Support using XGBoost as submodule.
* Fix windows
* Fix cpp tests on Windows
* Remove duplicated find_package.
* [r-package] cut CI-time dependency on craigcitro/r-travis (fixes#4348)
* Install R
* Install R on OSX
* Remove gfortran symlink
* Specify CRAN repo
* added more R dependencies needed for testing
* removed heavy R dependencies in CI
* fixed bug in env var, removed unnecessary apt installs of R
* fix to R installs
* make the assignments of HostDeviceVector exception safe.
* storing a dummy GPUDistribution instance in HDV for CPU based code.
* change testxgboost binary location to build directory.
* Fix test_gpu_coordinate.
* Use `gpu_coord_descent` in test.
* Reduce number of running rounds.
* Remove nthread.
* Use githubusercontent for r-appveyor.
* Use githubusercontent in travis r tests.
* Fix broken R test: Install Homebrew GCC
Missing GCC Fortran causes installation failure of a dependency package
(igraph)
* Register gfortran system-wide
* Use correct keg
* Set env vars to change compiler choice
* Do not break other Mac builds
* Nuclear option: symlink gfortran
* Use /usr/local/bin instead of /usr/bin
* Symlink library path too
* Update run_test.sh
* Fix failing Travis CI on Mac
Use Homebrew Addon + latest Mac image
* Use long command for pytest
* Downgrade OSX image to xcode9.3, to use Java 8
* Install pytest in Python 2 environment
* Remove clang-tidy from Travis
* 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#3402: wrong fid crashes distributed algorithm
The bug was introduced by the recent DMatrix refactor (#3301). It was partially
fixed by #3408 but the example in #3402 was still failing. The example in #3402
will succeed after this fix is applied.
* Explicitly specify "this" to prevent compile error
* Add regression test
* Add distributed test to Travis matrix
* Install kubernetes Python package as dependency of dmlc tracker
* Add Python dependencies
* Add compile step
* Reduce size of regression test case
* Further reduce size of test
CI tests were failing because wget prompts "the user" for a response
whenever the google test archive is already on the disk.
Fix: Use `-nc` option to skip download when the archive already
exists
* Use sparse page as singular CSR matrix representation
* Simplify dmatrix methods
* Reduce statefullness of batch iterators
* BREAKING CHANGE: Remove prob_buffer_row parameter. Users are instead recommended to sample their dataset as a preprocessing step before using XGBoost.
* Update dmlc-core submodule
* Fix dense_parser to work with the latest dmlc-core
* Specify location of Google Test
* Add more source files in dmlc-minimum to get latest dmlc-core working
* Update dmlc-core submodule
* Add interaction effects and cox loss
* Minimize whitespace changes
* Cox loss now no longer needs a pre-sorted dataset.
* Address code review comments
* Remove mem check, rename to pred_interactions, include bias
* Make lint happy
* More lint fixes
* Fix cox loss indexing
* Fix main effects and tests
* Fix lint
* Use half interaction values on the off-diagonals
* Fix lint again
* Fatal error if GPU algorithm selected without GPU support compiled
* Resolve type conversion warnings
* Fix gpu unit test failure
* Fix compressed iterator edge case
* Fix python unit test failures due to flake8 update on pip
* [jvm-packages] Fixed compilation on Windows
* [jvm-packages] Build the JNI bindings on Appveyor
* [jvm-packages] Build & test on OS X
* [jvm-packages] Re-applied the CMake build changes reverted by #2395
* Fixed Appveyor JVM build
* Muted Maven on Travis
* Don't link with libawt
* "linux2"->"linux"
Python2.x and 3.X use slightly different values for ``sys.platform``.
* Support histogram-based algorithm + multiple tree growing strategy
* Add a brand new updater to support histogram-based algorithm, which buckets
continuous features into discrete bins to speed up training. To use it, set
`tree_method = fast_hist` to configuration.
* Support multiple tree growing strategies. For now, two policies are supported:
* `grow_policy=depthwise` (default): favor splitting at nodes closest to the
root, i.e. grow depth-wise.
* `grow_policy=lossguide`: favor splitting at nodes with highest loss change
* Improve single-threaded performance
* Unroll critical loops
* Introduce specialized code for dense data (i.e. no missing values)
* Additional training parameters: `max_leaves`, `max_bin`, `grow_policy`, `verbose`
* Adding a small test for hist method
* Fix memory error in row_set.h
When std::vector is resized, a reference to one of its element may become
stale. Any such reference must be updated as well.
* Resolve cross-platform compilation issues
* Versions of g++ older than 4.8 lacks support for a few C++11 features, e.g.
alignas(*) and new initializer syntax. To support g++ 4.6, use pre-C++11
initializer and remove alignas(*).
* Versions of MSVC older than 2015 does not support alignas(*). To support
MSVC 2012, remove alignas(*).
* For g++ 4.8 and newer, alignas(*) is enabled for performance benefits.
* Some old compilers (MSVC 2012, g++ 4.6) do not support template aliases
(which uses `using` to declate type aliases). So always use `typedef`.
* Fix a host of CI issues
* Remove dependency for libz on osx
* Fix heading for hist_util
* Fix minor style issues
* Add missing #include
* Remove extraneous logging
* Enable tree_method=hist in R
* Renaming HistMaker to GHistBuilder to avoid confusion
* Fix R integration
* Respond to style comments
* Consistent tie-breaking for priority queue using timestamps
* Last-minute style fixes
* Fix issuecomment-271977647
The way we quantize data is broken. The agaricus data consists of all
categorical values. When NAs are converted into 0's,
`HistCutMatrix::Init` assign both 0's and 1's to the same single bin.
Why? gmat only the smallest value (0) and an upper bound (2), which is twice
the maximum value (1). Add the maximum value itself to gmat to fix the issue.
* Fix issuecomment-272266358
* Remove padding from cut values for the continuous case
* For categorical/ordinal values, use midpoints as bin boundaries to be safe
* Fix CI issue -- do not use xrange(*)
* Fix corner case in quantile sketch
Signed-off-by: Philip Cho <chohyu01@cs.washington.edu>
* Adding a test for an edge case in quantile sketcher
max_bin=2 used to cause an exception.
* Fix fast_hist test
The test used to require a strictly increasing Test AUC for all examples.
One of them exhibits a small blip in Test AUC before achieving a Test AUC
of 1. (See bottom.)
Solution: do not require monotonic increase for this particular example.
[0] train-auc:0.99989 test-auc:0.999497
[1] train-auc:1 test-auc:0.999749
[2] train-auc:1 test-auc:0.999749
[3] train-auc:1 test-auc:0.999749
[4] train-auc:1 test-auc:0.999749
[5] train-auc:1 test-auc:0.999497
[6] train-auc:1 test-auc:1
[7] train-auc:1 test-auc:1
[8] train-auc:1 test-auc:1
[9] train-auc:1 test-auc:1
Update the code coverage of the project on codecov for easy viewing.
Also the gcov on travis uses a different version which cannot
find the directory of the given files, and it needs to be specified
in the -o flag. Hence now we loop over the list of files and
run them independently.
